A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> We propose a novel and robust hashing paradigm that uses iterative geometric techniques and relies on observations that main geometric features within an image would approximately stay invariant under small perturbations. A key goal of this algorithm is to produce sufficiently randomized outputs which are unpredictable, thereby yielding properties akin to cryptographic MACs. This is a key component for robust multimedia identification and watermarking (for synchronization as well as content dependent key generation). Our algorithm withstands standard benchmark (e.g Stirmark) attacks provided they do not cause severe perceptually significant distortions. As verified by our detailed experiments, the approach is relatively media independent and works for audio as well. <s> BIB001 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> With the advent of Internet image authentication has become a central part of research in security. Since JPEG has recommended discrete cosine transform as one of the steps in image compression systems, a hash function which utilizes discrete cosine decomposition is desirable. In this paper, we propose a discrete cosine based hash function which distinguishes acceptable level of compression from image processing modifications such as Median filtering, Gaussian noise addition, and FMLR attack. To increase manipulation detection, we optimize the number of AC coefficients needed in smoothing. <s> BIB002 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Watermarking is largely used for copyright protection and fast search of images in databases. Another method for securely identifying images is to use hash functions. Digital signature standard, used in cryptosystem to dispute authentication documents, is based on hash functions. A digital signature is a bit stream dependent on key and content of document. For each document, the digital signature algorithm provides a unique output bit stream. In order to be efficient in images, the digital signature should be different if and only if the image content, and not the input bit stream, is different. Our new method is a one-way function for images. Using the radon transform and principal component analysis, we extract characteristics robust against geometrical transformation (rotation and scaling) and image processing attacks (compression, filtering, blurring). <s> BIB003 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> With the ever-increasing use of multimedia contents through electronic commerce and on-line services, the problems associated with the protection of intellectual property, management of large database and indexation of content are becoming more prominent. Watermarking has been considered as efficient means to these problems. Although watermarking is a powerful tool, there are some issues with the use of it, such as the modification of the content and its security. With respect to this, identifying content itself based on its own features rather than watermarking can be an alternative solution to these problems. The aim of fingerprinting is to provide fast and reliable methods for content identification. In this paper, we present a new approach for image fingerprinting using the Radon transform to make the fingerprint robust against affine transformations. Since it is quite easy with modern computers to apply affine transformations to audio, image and video, there is an obvious necessity for affine transformation resilient fingerprinting. Experimental results show that the proposed fingerprints are highly robust against most signal processing transformations. Besides robustness, we also address other issues such as pairwise independence, database search efficiency and key dependence of the proposed method. <s> BIB004 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> We propose an image hashing algorithm that is based on distributed compression principles. The algorithm assumes the availability of a robust feature vector extracted from the image. Then a suitable dither sequence is added to this feature vector, and the resulting dithered feature vector is compressed using distributed compression principles. We prove that the dither sequence can be used to guarantee information-theoretic security. Applications of our proposed secure image hashing scheme include video watermarking, image authentication, and image database management. <s> BIB005 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Image hash functions provide compact representations of images, which is useful for search and authentication applications. In this work, we have identified a general three step framework and proposed a new image hashing scheme that achieves a better overall performance than the existing approaches under various kinds of image processing distortions. By exploiting the properties of discrete polar Fourier transform and incorporating cryptographic keys, the proposed image hash is resilient to geometric and filtering operations, and is secure against guessing and forgery attacks. <s> BIB006 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Due to the desired non-invasive property, non-data hiding (called media hashing here) is considered to be an alternative to achieve many applications previously accomplished with watermarking. Recently, media hashing techniques for content identification have been gradually emerging. However, none of them are really resistant against geometrical attacks. In this paper, our aim is to propose a geometry-invariant image hashing scheme, which can be employed for content copy detection and tracing. Our system is mainly composed of three components: (i) robust mesh extraction; (iii) mesh-based robust hash extraction; and (iii) hash matching for similarity measurement. Exhaustive experimental results obtained from benchmark attacks have confirmed the performance of the proposed method <s> BIB007 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> In this paper we suggest viewing images (as well as attacks on them) as a sequence of linear operators and propose novel hashing algorithms employing transforms that are based on matrix invariants. To derive this sequence, we simply cover a two dimensional representation of an image by a sequence of (possibly overlapping) rectangles R/sub i/ whose sizes and locations are chosen randomly/sup 1/ from a suitable distribution. The restriction of the image (representation) to each R/sub i/ gives rise to a matrix A/sub i/. The fact that A/sub i/'s will overlap and are random, makes the sequence (respectively) a redundant and non-standard representation of images, but is crucial for our purposes. Our algorithms first construct a secondary image, derived from input image by pseudo-randomly extracting features that approximately capture semi-global geometric characteristics. From the secondary image (which does not perceptually resemble the input), we further extract the final features which can be used as a hash value (and can be further suitably quantized). In this paper, we use spectral matrix invariants as embodied by singular value decomposition. Surprisingly, formation of the secondary image turns out be quite important since it not only introduces further robustness (i.e., resistance against standard signal processing transformations), but also enhances the security properties (i.e. resistance against intentional attacks). Indeed, our experiments reveal that our hashing algorithms extract most of the geometric information from the images and hence are robust to severe perturbations (e.g. up to %50 cropping by area with 20 degree rotations) on images while avoiding misclassification. Our methods are general enough to yield a watermark embedding scheme, which will be studied in another paper. <s> BIB008 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Robust signal hashing defines a feature vector that characterizes the signal, independently of "nonsignificant" distortions of its content. When dealing with images, the considered distortions are typically due to compression or small geometrical manipulations. In other words, robustness means that images that are visually indistinguishable should produce equal or similar hash values. To discriminate image contents, a hash function should produce distinct outputs for different images. Our paper first proposes a robust hashing algorithm for still images. It is based on radial projection of the image pixels and is denoted the Radial hASHing (RASH) algorithm. Experimental results provided on the USC-SIPI dataset reveal that the proposed RASH feature vector is more robust and provides much stronger discrimination than a conventional histogram-based feature vector. The RASH vector appears to be a good candidate to build indexing algorithms, copy-detection systems, or content-based authentication mechanisms. To take benefit from the RASH vector capabilities, video content is summarized into key frames, each of them characterizing a video shot and described by its RASH vector. The resulting video hashing system works in real time and supports most distortions due to common spatial and temporal video distortions. <s> BIB009 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Media hashing is an alternative approach to many applications previously accomplished with watermarking. The major disadvantage of the existing media hashing technologies is their limited resistance to geometric attacks. In this paper, a novel geometric distortion-invariant image hashing scheme, which can be employed to perform copy detection and content authentication of digital images, is proposed. Our major contributions are threefold: (i) a mesh-based robust hashing function is proposed; (ii) a sophisticated hash database for error-resilient and fast matching is constructed; and (iii) the application scalability of our scheme for content copy tracing and authentication is studied. In addition, we further investigate several media hashing issues, including robustness and discrimination, error analysis, and complexity, with respect to the proposed image hashing system. Exhaustive experimental results obtained from benchmark attacks confirm the excellent performance of the proposed method. <s> BIB010 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Media hashing is an important resolving skill of copyright infringement. In this paper, a novel robust image hashing scheme is proposed. The set partitioning in hierarchical trees (SPIHT) algorithm, widely employed in image compression, is used to extract the identification information of images. The sorting pass of SPIHT records the spatial distribution of significant wavelet coefficients, termed the significance map. We build the hash values from the significance maps and the associated autocorrelograms. To verify the robustness of the proposed methods, experiments are conducted on the Stir mark benchmarking system. The proposed hash sequence in autocorrelogram achieves good performance with reasonable complexity. <s> BIB011 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> A wavelet-based robust database hashing scheme is analyzed with respect to its resilience against image modifications and hostile attacks. A method to construct a forgery is presented and possible countermeasures are discussed. <s> BIB012 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Image perceptual hashing has been proposed to identify or authenticate image contents in a robust way against distortions caused by compression, noise, common signal processing and geometrical modifications, while still holding a good discriminability for different ones in sense of human perception. We propose and compare four normalized block mean value based image perceptual hashing algorithms which demonstrate higher performances than other existing algorithms in robustness-anddiscriminalibility and simplicity for implementation. Overlapped blocking and rotation operations are employed to enhance the robustness to geometrical distortions. To evaluate the proposed algorithms? robustness and discriminability, given fixed modifications, identification ratio is used; and given fixed content classification, receiver operating curves is obtained. <s> BIB013 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> In this paper, we propose the use of non-negative matrix factorization (NMF) for image hashing. In particular, we view images as matrices and the goal of hashing as a randomized dimensionality reduction that retains the essence of the original image matrix while preventing intentional attacks of guessing and forgery. Our work is motivated by the fact that standard-rank reduction techniques, such as QR and singular value decomposition, produce low-rank bases which do not respect the structure (i.e., non-negativity for images) of the original data. We observe that NMFs have two very desirable properties for secure image hashing applications: 1) The additivity property resulting from the non-negativity constraints results in bases that capture local components of the image, thereby significantly reducing misclassification and 2) the effect of geometric attacks on images in the spatial domain manifests (approximately) as independent identically distributed noise on NMF vectors, allowing the design of detectors that are both computationally simple and, at the same time, optimal in the sense of minimizing error probabilities. Receiver operating characteristics analysis over a large image database reveals that the proposed algorithms significantly outperform existing approaches for image hashing. <s> BIB014 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> An image hash should be (1) robust to allowable operations and (2) sensitive to illegal manipulations and distinct queries. Some applications also require the hash to be able to localize image tampering. This requires the hash to contain both robust content and alignment information to meet the above criterion. Fulfilling this is difficult because of two contradictory requirements. First, the hash should be small and second, to verify authenticity and then localize tampering, the amount of information in the hash about the original required would be large. Hence a tradeoff between these requirements needs to be found. This paper presents an image hashing method that addresses this concern, to not only detect but also localize tampering using a small signature (< 1kB). Illustrative experiments bring out the efficacy of the proposed method compared to existing methods. <s> BIB015 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> In this paper, we propose a robust image hash algorithm by using the invariance of the image histogram shape to geometric deformations. Robustness and uniqueness of the proposed hash function are investigated in detail by representing the histogram shape as the relative relations in the number of pixels among groups of two different bins. It is found from extensive testing that the histogram-based hash function has a satisfactory performance to various geometric deformations, and is also robust to most common signal processing operations thanks to the use of Gaussian kernel low-pass filter in the preprocessing phase. <s> BIB016 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> The invariance relation existing in the non-negative matrix factorization (NMF) is used for constructing robust image hashes in this work. The image is first re-scaled to a fixed size. Low-pass filtering is performed on the luminance component of the re-sized image to produce a normalized matrix. Entries in the normalized matrix are pseudo-randomly re-arranged under the control of a secret key to generate a secondary image. Non-negative matrix factorization is then performed on the secondary image. As the relation between most pairs of adjacent entries in the NMF's coefficient matrix is basically invariant to ordinary image processing, a coarse quantization scheme is devised to compress the extracted features contained in the coefficient matrix. The obtained binary elements are used to form the image hash after being scrambled based on another key. Similarity between hashes is measured by the Hamming distance. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as Gaussian filtering, moderate noise contamination, JPEG compression, re-scaling, and watermark embedding. Hashes of different images have very low collision probability. Tampering to local image areas can be detected by comparing the Hamming distance with a predetermined threshold, indicating the usefulness of the technique in digital forensics. <s> BIB017 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> With the rapid development of multimedia technology, how to establish the integrity of images has become an unavoidable problem. The transform between a digital image and its hard-copy makes the problem more complex. To simplify the content based authentication, we propose a print-scan resistant image hashing algorithm based on the Radon and wavelet transform. The Radon transforms an image to its luminance distribution, before the wavelet extracts the relationship of the different areas from the luminance distribution. Experimental results show that our algorithm is not only robust to print-scan and other common content-preserving processing, but also discriminable to content changes. <s> BIB018 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> The perceptual image hashing is an emerging technique which can be used in image authentication and content-based image retrieval. Recently, several image hashing schemes based on Radon transform are proposed for image authentication and retrieval. These schemes have no random information to strengthen the security with image hashing. In this paper, we propose a simple key-based secure image hashing scheme by using radon transform and 1-D Discrete Cosine Transform (DCT). Particularly, we introduce a randomization on Radon transform and make the image hash more secure and more robust. Moreover, the discriminative capability is also confirmed in our experimental results. <s> BIB019 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> In order to design the image hash from human being's view, we capture the perceptual characters of the image using Gabor filter which can sense the directions in the image just like human's primary visual cortex. For a given image, we compute the reference scale, direction and block to make sure the final hash can resist against RST (Rotation, Scale, Translation) attacks while maintain the sensitivity to local malicious manipulations. In addition, it has another promising ability to locate the tampered image blocks, approximately determined tamper methods (delete, add, modify) and the original direction of each block. The ability is very useful in forensics. The experimental results show that the strategy of the reference metrics works quite well and it is much more effective than the other state of art image hash method. <s> BIB020 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Dimension reduction techniques, such as singular value decomposition (SVD) and nonnegative matrix factorization (NMF), have been successfully applied in image hashing by retaining the essential features of the original image matrix. However, a concern of great importance in image hashing is that no single solution is optimal and robust against all types of attacks. The contribution of this paper is threefold. First, we introduce a recently proposed dimension reduction technique, referred as Fast Johnson-Lindenstrauss Transform (FJLT), and propose the use of FJLT for image hashing. FJLT shares the low distortion characteristics of a random projection, but requires much lower computational complexity. Secondly, we incorporate Fourier-Mellin transform into FJLT hashing to improve its performance under rotation attacks. Thirdly, we propose a new concept, namely, content-based fingerprint, as an extension of image hashing by combining different hashes. Such a combined approach is capable of tackling all types of attacks and thus can yield a better overall performance in multimedia identification. To demonstrate the superior performance of the proposed schemes, receiver operating characteristics analysis over a large image database and a large class of distortions is performed and compared with the state-of-the-art image hashing using NMF. <s> BIB021 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> In this paper, a new image hashing scheme satisfying robustness and security is proposed. We exploit the property of dimensionality reduction inherent in compressive sensing/sampling (CS) for image hash design. The gained benefits include (1) the hash size can be kept small and (2) the CS-based hash is computationally secure. We study the use of visual information fidelity (VIF) for hash comparison under Stirmark attacks. We further derive the relationships between the hash of an image and both of its MSE distortion and visual quality measured by VIF, respectively. Hence, based on hash comparisons, both the distortion and visual quality of a query image can be approximately estimated without accessing its original version. We also derive the minimum distortion for manipulating an image to be unauthentic to measure the security of our scheme. <s> BIB022 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> In this paper, a new image hashing method using Zernike moments is proposed. This method is based on rotation invariance of magnitudes and corrected phases of Zernike moments. At first the input image is divided into overlapped blocks. Zernike moments of these blocks are calculated and then each of the amplitudes and phases of modified Zernike moments is then encoded into three bits to form the intermediate hash. Lastly, the final hash sequence is obtained by pseudo-randomly permuting the intermediate hash sequence. Similarity between hashes is measured with the Hamming distance. Experimental results show that this method is robust against most content-preserving attacks. The Hamming distance of Hashes between two different images is bigger than the threshold. This method can be used to detect tampering image, and can locate the tampered region in the image. <s> BIB023 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> This paper proposes a new robust and secure perceptual image hashing technique based on virtual watermark detection. The idea is justified by the fact that the watermark detector responds similarly to perceptually close images using a non embedded watermark. The hash values are extracted in binary form with a perfect control over the probability distribution of the hash bits. Moreover, a key is used to generate pseudo-random noise whose real values contribute to the randomness of the feature vector with a significantly increased uncertainty of the adversary, measured by mutual information, in comparison with linear correlation. Experimentally, the proposed technique has been shown to outperform related state-of-the art techniques recently proposed in the literature in terms of robustness with respect to image processing manipulations and geometric attacks. <s> BIB024 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Image authentication has become an emergency issue in the digital world as it can be easily tampered with the image editing techniques. In this paper, a novel robust hashing method for image authentication is proposed. The reported scheme first performs Radon transform (RT) on the image, and calculates the moment features which are invariant to translation and scaling in the projection space. Then discrete Fourier transform (DFT) is applied on the moment features to resist rotation. Finally, the magnitude of the significant DFT coefficients is normalized and quantized as the image hash bits. Experimental results show that the proposed algorithm can tolerate almost all the typical image processing manipulations, including JPEG compression, geometric distortion, blur, addition of noise, and enhancement. Compared with other approaches in the literature, the reported method is more effective for image authentication in terms of detection performance and the hash size. <s> BIB025 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Structural image features are exploited to construct perceptual image hashes in this work. The image is first preprocessed and divided into overlapped blocks. Correlation between each image block and a reference pattern is calculated. The intermediate hash is obtained from the correlation coefficients. These coefficients are finally mapped to the interval [0, 100], and scrambled to generate the hash sequence. A key component of the hashing method is a specially defined similarity metric to measure the “distance” between hashes. This similarity metric is sensitive to visually unacceptable alterations in small regions of the image, enabling the detection of small area tampering in the image. The hash is robust against content-preserving processing such as JPEG compression, moderate noise contamination, watermark embedding, re-scaling, brightness and contrast adjustment, and low-pass filtering. It has very low collision probability. Experiments are conducted to show performance of the proposed method. ::: ::: (This work was supported by the NSF of China (60773079, 60872116, and 60832010), the High-Tech Res. and Dev. Prog. of China (2007AA01Z477), the Innovative Res. Fdn. of Shanghai Univ. for Ph.D. Programs (shucx080148), and the Sci. Res. Fdn. of Guangxi Normal Univ. for Doctors.) <s> BIB026 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> A robust image hashing using locally linear embedding (LLE) is presented. The image is first rescaled to a fixed size. Then the image is divided into key-dependent overlapping blocks. Every blocks are reshaped to vectors and combined to the matrix. The LLE algorithm is applied to the matrix. The reconstruction weights coefficients reflect the intrinsic geometric properties of image and are invariant basicly to ordinary image processing and several geometric transformation. The quantization scheme is devised to compress the coefficients matrix and form the binary image hash. Test results indicate that the proposed method is robust against content-preserving modifications such as JPEG compression, filtering and affine transformation. Hashes of different images have very low collision probability. <s> BIB027 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Local feature points have been widely investigated in solving problems in computer vision, such as robust matching and object detection. However, its investigation in the area of image hashing is still limited. In this paper, we propose a novel shape-contexts-based image hashing approach using robust local feature points. The contributions are twofold: 1) The robust SIFT-Harris detector is proposed to select the most stable SIFT keypoints under various content-preserving distortions. 2) Compact and robust image hashes are generated by embedding the detected local features into shape-contexts-based descriptors. Experimental results show that the proposed image hashing is robust to a wide range of distortions and attacks, due to the benefits of robust salient keypoints detection and the shape-contexts-based feature descriptors. When compared with the current state-of-the-art schemes, the proposed scheme yields better identification performances under geometric attacks such as rotation attacks and brightness changes, and provides comparable performances under classical distortions such as additive noise, blurring, and compression. Also, we demonstrate that the proposed approach could be applied for image tampering detection. <s> BIB028 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Abstract The rapid development of multimedia technology has resulted in a rising rate on digital unauthorized utilization and forgery, which makes the situation of image authentication increasingly severe. A novel strong image hashing scheme is proposed based on wave atom transform, which can better authenticate images by precisely distinguishing the malicious tampered images from the non-maliciously processed ones. Wave atom transform is employed since it has significantly sparser expansion and better characteristics of texture feature extraction than other traditional transforms. For better detection sensitivity, gray code is applied instead of natural binary code to optimize the hamming distance. Randomizations are also performed using Renyi chaotic map for the purposes of secure image hashing and key sensitivity. The experimental results show that the proposed strong scheme is robust to non-malicious content-preserving operations and also fragile to malicious content-altering operations. The scheme also outperforms DCT and DWT based schemes in terms of receiving operating characteristic (ROC) curves. Moreover, to provide an application-defined tradeoff, a security enhancement approach based on Renyi map is presented, which can further protect the integrity and secrecy of images. <s> BIB029 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is randomized to facilitate secure feature extraction. Particularly, a novel dithered-LVQ-based quantization scheme is proposed for robust hashing. The dithered-LVQ-based quantization scheme is well suited for robust hashing with several desirable features, including better tradeoff between robustness and discrimination, higher randomness, and secrecy, which are validated by analytical and experimental results. The performance of the proposed hashing algorithm is evaluated over a test image database under various content-preserving manipulations. The proposed hashing algorithm shows superior robustness and discrimination performance compared with other state-of-the-art algorithms, particularly in the robustness against rotations (of large degrees). <s> BIB030 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Image hashing is a novel technology of multimedia and finds many applications such as image retrieval, image copy detection, digital watermarking and image indexing. This paper proposes a multiple histograms based image hashing, which can reach an acceptable trade-off between rotation robustness and discrimination. The proposed hashing is done by converting the input image into a normalized image, dividing it into different rings, extracting ring-based histograms and compressing them by discrete wavelet transform. Hash similarity is evaluated by L2 norm. Experiments show that our hashing is robust against content-preserving manipulations such as JPEG compression, watermark embedding, scaling, rotation, brightness and contrast adjustment, gamma correction and Gaussian low-pass filtering. Receiver operating characteristics (ROC) curve comparisons indicate that our hashing has better performances than two existing algorithms in classification between perceptual robustness and discriminative capability. <s> BIB031 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Currently, digital images and videos have high importance because they have become the main carriers of information. However, the relative ease of tampering with images and videos makes their authenticity untrustful. Digital image forensics addresses the problem of the authentication of images or their origins. One main branch of image forensics is passive image forgery detection. Images could be forged using different techniques, and the most common forgery is the copy-move, in which a region of an image is duplicated and placed elsewhere in the same image. Active techniques, such as watermarking, have been proposed to solve the image authenticity problem, but those techniques have limitations because they require human intervention or specially equipped cameras. To overcome these limitations, several passive authentication methods have been proposed. In contrast to active methods, passive methods do not require any previous information about the image, and they take advantage of specific detectable changes that forgeries can bring into the image. In this paper, we describe the current state-of-the-art of passive copy-move forgery detection methods. The key current issues in developing a robust copy-move forgery detector are then identified, and the trends of tackling those issues are addressed. <s> BIB032 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> This paper proposes a robust image hashing method in discrete Fourier domain that can be applied in such fields as image authentication and retrieval. In the pre-processing stage, image resizing and total variation based filtering are first used to regularize the input image. Then the secondary image is obtained by the rotation projection, and the robust frequency feature is extracted from the secondary image after discrete Fourier transform. More sampling points are chosen from the low- and middle-frequency component to represent the salient content of the image effectively, which is achieved by the non-uniform sampling. Finally, the intermediate sampling feature vectors are scrambled and quantized to produce the resulting binary hash securely. The security of the method depends entirely on the secret key. Experiments are conducted to show that the present method has satisfactory robustness against perceptual content-preserving manipulations and has also very low probability for collision of the hashes of distinct images. <s> BIB033 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Recently the methods based on bag-of-visual words have become very popular in near-duplicate retrieval and content identification. However, obtaining the visual vocabulary by quantization is very time-consuming and unscalable to large databases. In this paper, we propose a fast copy detection method which uses local image fingerprints to define visual words. To construct the fingerprint, a 32-bit vector is extracted from the local description and then converted into a number which is used to define the visual word. Then, a histogram intersection is employed to measure the similarity between two images. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. Furthermore, the fingerprint-defined visual words are more discriminative and precise than the clustering-defined visual words because the vocabulary size could be large enough while maintaining high efficiency. Visual words with strong discriminability can distinguish copies from similar objects, which can reduce the number of false positives and improve the precision and efficiency. The evaluation shows that our approach significantly outperforms state-of-the-art methods. <s> BIB034 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Abstract Conventional image hash functions only exploit luminance components of color images to generate robust hashes and then lead to limited discriminative capacities. In this paper, we propose a robust image hash function for color images, which takes all components of color images into account and achieves good discrimination. Firstly, the proposed hash function re-scales the input image to a fixed size. Secondly, it extracts local color features by converting the RGB color image into HSI and YCbCr color spaces and calculating the block mean and variance from each component of the HSI and YCbCr representations. Finally, it takes the Euclidian distances between the block features and a reference feature as hash values. Experiments are conducted to validate the efficiency of our hash function. Receiver operating characteristics (ROC) curve comparisons with two existing algorithms demonstrate that our hash function outperforms the assessed algorithms in classification performances between perceptual robustness and discriminative capability. <s> BIB035 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> A robust hashing method is developed for detecting image forgery including removal, insertion, and replacement of objects, and abnormal color modification, and for locating the forged area. Both global and local features are used in forming the hash sequence. The global features are based on Zernike moments representing luminance and chrominance characteristics of the image as a whole. The local features include position and texture information of salient regions in the image. Secret keys are introduced in feature extraction and hash construction. While being robust against content-preserving image processing, the hash is sensitive to malicious tampering and, therefore, applicable to image authentication. The hash of a test image is compared with that of a reference image. When the hash distance is greater than a threshold τ1 and less than τ2, the received image is judged as a fake. By decomposing the hashes, the type of image forgery and location of forged areas can be determined. Probability of collision between hashes of different images approaches zero. Experimental results are presented to show effectiveness of the method. <s> BIB036 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Perceptual hashing is conventionally used for content identification and authentication. It has applications in database content search, watermarking and image retrieval. Most countermeasures proposed in the literature generally focus on the feature extraction stage to get robust features to authenticate the image, but few studies address the perceptual hashing security achieved by a cryptographic module. When a cryptographic module is employed [1], additional information must be sent to adjust the quantization step. In the perceptual hashing field, we believe that a perceptual hashing system must be robust, secure and generate a final perceptual hash of fixed length. This kind of system should send only the final perceptual hash to the receiver via a secure channel without sending any additional information that would increase the storage space cost and decrease the security. For all of these reasons, in this paper, we propose a theoretical analysis of full perceptual hashing systems that use a quantization module followed by a crypto-compression module. The proposed theoretical analysis is based on a study of the behavior of the extracted features in response to content-preserving/content-changing manipulations that are modeled by Gaussian noise. We then introduce a proposed perceptual hashing scheme based on this theoretical analysis. Finally, several experiments are conducted to validate our approach, by applying Gaussian noise, JPEG compression and low-pass filtering. <s> BIB037 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Conventional image hashing maps invariant features of each digital image into a unique, compact, robust, and secure signature, which can be used as an index for fast content identification and copyright protection. This paper addresses an important issue of compressing the real-valued image hashes into short binary signatures, which can support fast image identification using Hamming distance metrics. The proposed binary image hashing approach presents a fundamental departure from existing methods: Prior information from virtual image distortions and attacks is explored the first time in image hash generation. More specifically, the proposed scheme takes advantages of the extended hash feature space from virtual distortions and attacks and generates the binary signature for each image based on spectral embedding. Since the objective function to learn the embedding is designed to both preserve local similarity between distorted copies of the same image and to distinguish visually distinct images, the generated binary image hash is more robust compared with the one using conventional quantization-based compression approaches. Further, the proposed method can be generalized to combine different types of image hashes to generate a fixed-length binary signature. Our experimental results demonstrate that the proposed binary image hash by combining different real-valued image hashes is more robust against various distortions and it is computationally efficient for image similarity comparison using Hamming metrics. <s> BIB038 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Abstract The content authenticity is critical for secure transmission of multimedia information. As a promising solution, perceptual image hashing has gain great attention. In this paper, we develop a novel algorithm for generating an image hash based on invariants of radial Tchebichef moments. The idea is justified by the fact that the radial Tchebichef moments represent the image under the orthogonal kernel, which has the desirable qualities of orthogonality and robustness. The hash values are achieved by adaptive quantization of the invariants of radial Tchebichef moments, then the random Gay code is applied in the discrete–binary conversion stage to enhance the expected discriminability. Experiments are conducted to show that the proposed hashing algorithm has superior robustness and discrimination performance compared with other state-of-the-art algorithms, in terms of receiving operating characteristic (ROC) curves. <s> BIB039 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> This paper designs an efficient image hashing with a ring partition and a nonnegative matrix factorization (NMF), which has both the rotation robustness and good discriminative capability. The key contribution is a novel construction of rotation-invariant secondary image, which is used for the first time in image hashing and helps to make image hash resistant to rotation. In addition, NMF coefficients are approximately linearly changed by content-preserving manipulations, so as to measure hash similarity with correlation coefficient. We conduct experiments for illustrating the efficiency with 346 images. Our experiments show that the proposed hashing is robust against content-preserving operations, such as image rotation, JPEG compression, watermark embedding, Gaussian low-pass filtering, gamma correction, brightness adjustment, contrast adjustment, and image scaling. Receiver operating characteristics (ROC) curve comparisons are also conducted with the state-of-the-art algorithms, and demonstrate that the proposed hashing is much better than all these algorithms in classification performances with respect to robustness and discrimination. <s> BIB040 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> We proposed a scheme to fast retrieval similar images.SIFT features are converted to binary strings to shorten the time needed for feature matching.A fast retrieval method using hash technique is proposed.The proposed scheme possesses corresponding distinguishing ability as compared to the original SIFT method. The well-known SIFT is capable of extracting distinctive features for image retrieval. However, its matching is time consuming and slows down the entire process. In the SIFT matching, the Euclidean distance is used to measure the similarity of two features, which is expensive because it involves taking square root. Moreover, the scale of the image database is usually too large to adopt linear search for image retrieval. To improve the SIFT matching, this paper proposes a fast image retrieval scheme transforms the SIFT features to binary representations. The complexity of the distance calculation is reduced to bit-wise operation and the retrieval time is greatly decreased. Moreover, the proposed scheme utilizes hashing for retrieving similar images according to the binarized features and further speeds up the retrieval process. The experiment results show the proposed scheme can retrieve images efficiently with only a little sacrifice of accuracy as compared to SIFT. <s> BIB041 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Perceptual image hash has been widely investigated in an attempt to solve the problems of image content authentication and content-based image retrieval. In this paper, we combine statistical analysis methods and visual perception theory to develop a real perceptual image hash method for content authentication. To achieve real perceptual robustness and perceptual sensitivity, the proposed method uses Watson’s visual model to extract visually sensitive features that play an important role in the process of humans perceiving image content. We then generate robust perceptual hash code by combining image-block-based features and key-point-based features. The proposed method achieves a tradeoff between perceptual robustness to tolerate content-preserving manipulations and a wide range of geometric distortions and perceptual sensitivity to detect malicious tampering. Furthermore, it has the functionality to detect compromised image regions. Compared with state-of-the-art schemes, the proposed method obtains a better comprehensive performance in content-based image tampering detection and localization. <s> BIB042 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> We investigate the use of LLE in image hashing.We find that embedding vector variances of LLE are approximately linearly changed by content-preserving operations.We propose a robust image hashing based on this LLE property.Our hashing outperforms some notable algorithms in classification performances. Locally linear embedding (LLE) has been widely used in data processing, such as data clustering, video identification and face recognition, but its application in image hashing is still limited. In this work, we investigate the use of LLE in image hashing and find that embedding vector variances of LLE are approximately linearly changed by content-preserving operations. Based on this observation, we propose a novel LLE-based image hashing. Specifically, an input image is firstly mapped to a normalized matrix by bilinear interpolation, color space conversion, block mean extraction, and Gaussian low-pass filtering. The normalized matrix is then exploited to construct a secondary image. Finally, LLE is applied to the secondary image and the embedding vector variances of LLE are used to form image hash. Hash similarity is determined by correlation coefficient. Many experiments are conducted to validate our efficiency and the results illustrate that our hashing is robust to content-preserving operations and reaches a good discrimination. Comparisons of receiver operating characteristics (ROC) curve indicate that our hashing outperforms some notable hashing algorithms in classification between robustness and discrimination. <s> BIB043 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Robustness and discrimination are two of the most important objectives in image hashing. We incorporate ring partition and invariant vector distance to image hashing algorithm for enhancing rotation robustness and discriminative capability. As ring partition is unrelated to image rotation, the statistical features that are extracted from image rings in perceptually uniform color space, i.e., CIE L*a*b* color space, are rotation invariant and stable. In particular, the Euclidean distance between vectors of these perceptual features is invariant to commonly used digital operations to images (e.g., JPEG compression, gamma correction, and brightness/contrast adjustment), which helps in making image hash compact and discriminative. We conduct experiments to evaluate the efficiency with 250 color images, and demonstrate that the proposed hashing algorithm is robust at commonly used digital operations to images. In addition, with the receiver operating characteristics curve, we illustrate that our hashing is much better than the existing popular hashing algorithms at robustness and discrimination. <s> BIB044 </s> A Review of Hashing based Image Copy Detection Techniques <s> Categorization of hashing-based image copy detection techniques <s> Abstract Image hashing is a novel technology of multimedia processing, and finds many applications, such as image forensics, image retrieval and image indexing. Conventional image hashing algorithms have limitations in reaching desirable classification performances between rotation robustness and discrimination. Aiming at this issue, we propose a robust image hashing based on color vector angle and Canny operator. Specifically, our hashing firstly converts input image to a normalized image by interpolation and Gaussian low-pass filtering. And then, color vector angles and image edges are both extracted from the normalized image. Finally, statistical features incorporating color vector angles and image edges are calculated to form image hash. We conduct experiments with 2762 images to validate efficiency of our hashing. The experimental results show that our hashing is robust against normal digital processing, such as image rotation, brightness/contrast adjustment and JPEG compression, and reaches good discrimination. Receiver operating characteristics (ROC) curve comparisons with some state-of-the-art algorithms indicate that our hashing outperforms these compared algorithms in classification performances between robustness and discriminative capability. <s> BIB045
A good number of articles have been published proposing hashing-based image copy detection techniques. These techniques vary in terms of the key methodology employed as well as the overall mechanism. To provide a comprehensive review of these techniques, it is helpful to classify them into different categories depending upon the mechanism used for extracting the image features BIB032 . The techniques are grouped by the basic transformations used along with a few other categories (Table 1) . It is worth pointing out that a few of the reviewed techniques employ more than one mechanism. In such cases, the techniques were placed in the category that it used the most. Discrete Cosine Transform (DCT) based. Algorithms that employ DCT coefficients performed well in classification and in detecting duplicate copies [18] . T a n g et al. [18] proposed image hashing based on dominant DCT coefficients for image identification. K a i l a s a n a t h a n, N a i n i and O g u n b o n a BIB002 proposed a compression-tolerant DCT image hash where the hash is constructed by considering the wavelet basis. R o o v e r et al. BIB009 proposed hashing in which radial projections of the pixel luminance values based on DCT coefficients are used for hash generation. Results indicate that RASH vectors are specific to a particular image. T a n g, Wan and Z h a n g  proposed a technique based on a dictionary and Non-negative Matrix Factorization (NMF) to generate image hash for identification. The proposed mechanism has a low collision probability. Longjiang et al. used a robust approach based on sign extraction of the DCT coefficients matrix of each rectangle to generate the final hash. The advantage of using DCT for feature generation is its simplicity and reduction of the DCT feature vector. However, DCT-based techniques fail against geometric transformations BIB032 . Discrete Fourier Transform (DFT) based. The main advantage of using DFTbased techniques is their resilience to content-preserving modifications like geometric and filtering distortions  . Q i n and C h a n g BIB033 proposed a hashing technique based on DFT and non-uniform sampling. The proposed technique is robust to elementary image processing operations. S w a m i n a t h a n  proposed an algorithm for hash generation using DFT features and controlled randomization. The hash is claimed to be secure against estimation and forgery. DFT-based techniques are resilient to various content-preserving operations while having low collision probability though the algorithms in this category are more complex than DCT-based algorithms  . S w a m i n a t h a n, M a o and Wu BIB006 proposed an image hashing approach in which DFT is applied to the preprocessed image, which later quantized and compressed to get the final hash. The proposed approach is claimed to be resilient to common geometric and filtering operations. Discrete Wavelet Transform (DWT) based. The algorithms discussed in this section have been proven to exhibit good time-frequency localization property. A h m e d, S i y a l and A b b a s  proposed a secure and robust hash-based scheme based on DWT where permutation sequence is used to obtain the final hash. Proposed algorithms offer robustness against non-malicious distortions such as low-pass and high-pass filtering  . Lu and H s u BIB010 and Lu et al. BIB007 proposed hashing in which DWT is applied to the original image and Lowest frequency sub-band (LL) is selected for further hash generation. Lu and H s u BIB010 extend their work given in BIB007 where extensive analysis of hashing is done for robustness, discrimination, error analysis and complexity. M i c h a k and V e n k a t e s a n BIB001 proposed a robust image hash based on iterative geometric techniques. M e x i n e r and U h l BIB012 extend image hashing proposed by BIB001 where pseudorandom number generator is used to produce the secure hash. M o n g a and E v a n s  used feature points to generate an image hash, which works well for by avoiding misclassification. V e n k a t e s a n et al.  proposed a novel indexing technique in which, each subband of the wavelet decomposition is randomly tiled into small rectangles to generate the image hash. Y a n g and C h e n BIB011 proposed a novel image hash based on the locations of significant wavelet coefficients. K a r s h, L a s k a r and A d i t i  propose image hashing based on DWT-SVD (DWT-Singular Value Decomposition) where V component of HSV color space is used for hash generation. In summary, image-hashing systems based on the LL sub-band wavelet coefficients are not robust to change in brightness, contrast enhancement, etc. Discarding the LL-band coefficients can improve the robustness and at times it may fail to detect tampering that involves modification of gray values  . Radon Transform (RT) based. The algorithms in this category are based on the Radon transform in which values of the projection signal is used to generate the image features which are robust to translation, scaling and rotation BIB025 . Lei, W a n g and H u a n g BIB025 proposed a novel image hashing technique based on the Discrete Cosine Transform (DFT) and Radon transform. The algorithm performs well in detecting image copies with a small size of hash. L e f e b v r e, C r y z and M a c q BIB003 proposed a robust approach based on the Radon transform and Principal Component Analysis (PCA). L e f e b v r e, M a c q and L e g a t  present a compression and collision resilient algorithm based on the Radon transform named as RASH. Seo et al. BIB004 generated image fingerprints by using Radon transformation. W u, Z h o u and N i u BIB018 propose a hash algorithm based on radon and wavelet transform. The proposed mechanism shows resistance towards content changes. Ou and R h e e BIB019 proposed a five-step hashing technique based on the Radon transform. The mechanism claims to be robust and exhibits high discriminability while using a small hash of 240 bits. Unfortunately, Radon transform is not resistant to all the geometric transformations such as shifting, shearing, etc. BIB018 . However, this can be tolerated keeping in view that Radon transform works well for rotation, scaling and translation. Scale Invariant Feature Transform (SIFT) based. SIFT-based techniques have proven to be more robust and effective compared to other techniques. However, their most significant drawback is the large size of the feature vector, which slows down the copy detection process BIB041 . Generating smaller SIFT features is a potential solution to such a problem. C h e n and H s i e h BIB041 proposed an algorithm where 128-dimensional SIFT features are applied to the pre-processed image to produce the image hash. The proposed technique considerably reduces the execution time with a minor loss in accuracy as compared to the plain SIFT. H e f ei L i n g et al. BIB034 proposed a unique image hashing technique based on the fingerprint of local visual words. The technique surpasses similar mechanisms in terms of precision and efficiency. Lv and W a n g BIB028 propose an algorithm similar to BIB034 where Harris detector is used to select the most stable key-points, which are less susceptible to image processing attacks after applying SIFT. To inherit the security of hash functions, a secret key is incorporated into either feature extraction or compression or both to make the hashes more unpredictable. SIFT-based hashing methods exhibit encouraging performance under rotation attacks and change in brightness but exhibit less-than-satisfactory performance against additive noise, blurring and compression BIB028 . Other transformations. The algorithms in this category are based on different transformations like Log-polar transformation that is robust to large angle rotation operations. Wave-atom transform outperforms DCT and DWT based schemes in distinguishing spiteful tampering from non-spiteful tampering BIB029 . Gabor filters can be used to produce a robust and rotation-invariant hash. O u y a n g, C o a t r i e u x and S h u  propose the use of Quaternion Discrete Fourier Transform (QDFT) and Log-Polar Transform (LPT) for hashing. The proposed mechanism performs well as compared to similar techniques in terms of robustness against content-preserving operations and shows good sensitivity to non-authorized image content alterations. M a l k i n and V e n k a t e s a n  proposed image hashing approach based on Randlet transform to produce a key-based randomized digest. Randlet transform is built with structured randomness and hence gives good performance compared to the wavelet transform for image identification  . Li et al. BIB030 proposed a robust image hashing method based on random Gabor filter and dithered lattice vector quantization, where the gray code is employed to enhance the robustness of the hash function. L i u et al. BIB029 use image hashing where an input image is decomposed into five scale bands through wave atom transform. The mechanism performs better as compared to DCT and DWT based techniques. L i u and X i a o  proposed an image hashing approach based on Log-polar mapping and Contourlet transform for identification. W a n g et al. BIB020 proposed a robust perceptual hashing technique using Gabor filters by using three reference matrices. Block-based. The algorithms in this category are based on creating blocks within images for extracting image features. T a n g et al. BIB035 proposed a robust hashing technique for color images, where Euclidean distance is used to generate an n-integer-based hash value. T a n g et al.  also proposed block-based robust image hashing based on color-vector angles and DWT. The proposed mechanism is robust to normal digital operations including rotation up to 5 o . Y a n g, Gu and N i u BIB013 proposed four hashing algorithms wherein the first two algorithms are based on normalized block mean values while the next two utilize the rotation operation. The proposed mechanism works well against rotation up to 10 o except some basic image processing attacks. Algorithms in this category are robust against common image manipulations including watermark embedding, JPEG compression, contrast adjustment, brightness adjustment, Gaussian blur, gamma correction, scaling and small angle rotation BIB035 . Dimensionality-reduction based. This section includes techniques based on three different types of mechanisms, Non-negative Matrix Factorization (NMF), Singular Value Decomposition (SVD) and Fast Johnson-Lindenstrauss Transform (FJLT). H e r n a n d e z and K u r k o s k i  proposed image hashing in which after preprocessing, SVD is applied to get U S V components for hash generation. K o z a t, V e n k a t e s a n and M i c h a k BIB008 present two different scenarios of a generic hashing scheme known as SVD-SVD and DCT-SVD hashing. Lv and W a n g BIB021 proposed a novel image hash algorithm based on the variation of FJLT named as RI-FJLT to improve the performance under rotation attacks. M o n g a and M i c h a k BIB014 proposed two image hashing techniques named as NMF-NMF and NMF-NMF-SQ. The proposed techniques give outstanding security and robustness against a good number of image processing operations. T a n g et al. BIB017 proposed a robust hashing method based on NMF, which exhibits a low collision probability. K a r s h, L a s k a r and R i c h h a r i y a  proposed image hashing based on ring-based projected gradient-NMF and local features. The method is robust to content preserving operations and is capable of localizing the counterfeit area. Primarily SVD and NMF are applied to reduce the size of the feature matrices and to generate the final hash. SVD specially increases robustness against rotation and scaling. Fourier-Mellin transform is combined with FJLT to improve the performance towards rotation BIB021 . Feature-based. Algorithms in this category are based on structural features, visual model features, local features, and feature points. To extract structural features, a reference pattern is generated BIB026 . Some algorithms use Watson's visual model to extract visually complex features. Local features and feature point methods are also used to prove their relevance in copy detection. M o n g a and E v a n s  use an iterative feature detector to extract important geometry preserving feature points. Probabilistic quantization is further used on the derived features to enhance perceptual robustness. R o y and Su BIB015 proposed a robust hash mechanism for detecting image tampering. T a n g et al. BIB026 constructed a perceptual image hash based on structural image features. The mechanism is sensitive to visually unacceptable alterations of the image and has a low collision probability. X i a o f e n g et al. BIB042 proposed a visual model based on perceptual image hashing. This category makes an important contribution to the body of literature in that it proves the advantage of selecting local features over global features for copy detection. Moment-based. Algorithms in this category use translation, scale, and rotation invariant Tchebichef and Zernike moments for hash generation. Moment-based techniques have proved to be robust against translation, scaling, and rotation and hence are widely used in classification, image matching, character recognition and authentication systems  . T a n g, D a i and Z h a n g  proposed a perceptual hashing method for color images using invariant moments, which are invariant to translation, scaling and rotation and have been widely used in image classification and image matching. C h e n, Yu and F e n g BIB039 proposed a novel image hashing based on magnitudes of radial Tchebichef moments, which is resilient to image rotation. Z h a o et al. BIB036 proposed perceptual image in which Zernike moments of Y and (Cb-Cr) color components are calculated to produce the final hash. Z h a o and W e i BIB023 proposed image hashing based on Zernike moments where bits of all different blocks are combined to form the intermediate hash, which is finally pseudorandomly permuted to produce the final hash. Ring-based. The algorithms in this category are grouped with a reason that the image pixels of each ring is almost unchanged after rotation  . Different mechanisms such as image histogram, entropy, and NMF are used to generate the unique hash. T a n g et al. BIB044 proposed an image hashing based on ring partition and invariant vector distance. Here, the L* component of L*a*b* color model is used for hash generation. T a n g et al. BIB031 proposed the use of multiple histograms for hashing. The normalized image is divided into different rings with equal area, and ring-based histogram features are extracted to make the hash resilient to rotation. The proposed mechanism is resilient to rotation of any arbitrary angle. T a n g et al.  proposed another important hashing based on ring-based entropies. The proposed technique performs well as compared to others in terms of time complexity. T a n g, X. Z h a n g and S. Z h a n g BIB040 proposed a perceptual robust hashing based on ring partition and NMF. Here, the Y component of the image is divided into seven rings to form a secondary image which is used to compute the image hash. These algorithms show good robustness against rotation and good discriminative capability apart from being robust to basic image processing attacks BIB040 . Other algorithms. This category contains algorithms which are based on a wide variety of mechanisms such as Locally Linear Embedding (LLE) BIB037 proposed a secure perceptual hashing method based on quantization step analysis. The proposed mechanism is resilient to content-preserving manipulations. J o h n s o n and R a m c h a n d r a n BIB005 proposed a dither-based secure image hashing for image identification. K a n g, Lu and H s u BIB022 proposed a robust image hashing based on compressive image sensing. The key benefits of this mechanism include small and computationally secure image hash. K h e l i f i and J i a n g BIB024 proposed robust hashing in which a simple high-pass filter is applied horizontally and vertically to the input image to generate its filtered versions. Lv and W a n g BIB038 proposed a semiSupervised Spectral Embedding (SSE) mechanism for compressing real-valued intermediate image hashes into short robust binary image hashes. Sun, Y a n and D i n g BIB027 and T a n g et al. BIB043 proposed the use of LLE for formulating an image hash, which is used for image identification. X i a n g, K i m and H u a n g BIB016 proposed an image hashing algorithm based on image histogram. Z o u et al.  proposed an image copy detection framework which consists of both online and offline stages for hash generation based on non-negative sparse coding. T a n g et al. BIB045 proposed image hashing based on color vector angle and canny operator.