A Review of Hashing based Image Copy Detection Techniques <s> Execution time <s> Image robust hashing is related to cryptographic hash functions. In contrast to cryptographic hash functions this robust digest is sensitive only to perceptual change. Minor changes, which are not affecting the perception, do not result in a different hash. Image robust hashing is used in content-based retrieval, monitoring, and filtering. In this paper an image robust hashing method based on sign bit from the domain of Discrete Cosine Transform (DCT) is presented. DCT is widely used in image processing and video processing, e.g. for compression or digital watermarking. From an image with reduced dimensions we apply 2-D DCT to derive an initial feature vector. The sign bit of this feature vector is extracted to form an intermediate hash. The intermediate hash can be incorporated into some security mechanism to derive a final hash. The advantages of the sign signal in DCT domain are verified in experiments evaluating robustness (e.g. against operations like lossy compression, scaling and cropping) and discriminability. The results show a better performance of the proposed method in this paper than an existing method. <s> BIB001 </s> A Review of Hashing based Image Copy Detection Techniques <s> Execution time <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> BIB002 </s> A Review of Hashing based Image Copy Detection Techniques <s> Execution time <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> BIB003 </s> A Review of Hashing based Image Copy Detection Techniques <s> Execution time <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> BIB004 </s> A Review of Hashing based Image Copy Detection Techniques <s> Execution time <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> BIB005 </s> A Review of Hashing based Image Copy Detection Techniques <s> Execution time <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> BIB006
In this section, the execution time for generating the image hashes of the compared algorithms are given. The time taken to extract image hashes of 200 images for the discriminative analysis is noted for each of the algorithms and then the average time for generating an image hash is found. 0.162  0.1 0.27 BIB005 0.61 BIB002 3.422 12.8 BIB006 0.286 0.28  0 The size of the feature vector of the resulting hash is one of the important parameters for any of the hashing system. Generally, a large hash size implies that a longer processing time is needed for comparison BIB006 . Also, storing large-sized hash for each of the images of the dataset will involve a significant storage overhead BIB004 . Therefore, it is desirable to have a small hash size to ensure optimum performance of the algorithm while also optimizing other affected parameters. Table 13 provides the hash sizes for the different algorithms reviewed in this paper. SIFT BIB004 has the smallest hash size of 32 bits; however, other performance parameters were not very encouraging. It is important to note that the table below consists of hash size in two different units, i.e., binary [18,  BIB001 and decimal digits BIB006 BIB003  . Ideally, an attempt is made to realize the smallest hash size possible without compromising on various performance parameters.