A Review of Hashing based Image Copy Detection Techniques <s> Hashing based image copy detection <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> BIB001 </s> A Review of Hashing based Image Copy Detection Techniques <s> Hashing based image copy detection <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> BIB002 </s> A Review of Hashing based Image Copy Detection Techniques <s> Hashing based image copy detection <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> BIB003
Hashing-based image copy detection techniques are based on the underlying principle of content-based image copy detection. However, features are here compressed to form a short binary code termed as an image hash. Researchers used different ways to convert a feature to its corresponding image hash namely quantization, binary representation, vector distance etc. This image hash serves as a signature for the original image  . After extracting a similar signature from a test image, the signature of both original and the test image is compared. A copy detection system is expected to find a set of matching images for a given original image when the difference between their hash values is lesser than a pre-defined threshold  BIB002 . However, if the threshold is too low, then a copy that has undergone minor changes will likely be skipped as being different. On the other hand, if the threshold is too high, then images which are similar but not copies will also be falsely matched and categorized as copies. Therefore, an optimal threshold value needs to be found such that a hashing mechanism results in a copy detection system that has a high true positive rate and a low false positive rate BIB001 . A block diagram illustrating the working of a typical hashing-based image copy detection is shown in Fig. 2 . The original image is here firstly pre-processed under operations such as resizing, filtering and color space conversion. Next, the unique features of the image are extracted by using any of the feature extraction mechanisms like DCT. Lastly, extracted features are compressed to form a binary hash, H. The same feature extraction mechanism is then applied to the modified image to generate its hash H. H and H are then compared using a similarity metric, and a threshold value is used to define the outcome of a similarity metric  . To make the hash secure, the generated hash can be encrypted using a secure key. This ensures that the hash is keydependent and can only be used by an authorized user for verification BIB003 . In general, an ideal image hash function should satisfy two basic properties -robustness and discriminability [18] . In robustness, visually identical images should produce similar hashes irrespective of their digital representation. For discriminability, different images must return a significantly different hash. Generally, these two properties conflict with each other. Therefore, to balance these properties, an optimum threshold value must be choosen so that the system can differentiate between images that are copies and those that are different  .