A Review of Hashing based Image Copy Detection Techniques <s> Discriminability <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> Discriminability <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> BIB002 </s> A Review of Hashing based Image Copy Detection Techniques <s> Discriminability <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> BIB003 </s> A Review of Hashing based Image Copy Detection Techniques <s> Discriminability <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> BIB004 </s> A Review of Hashing based Image Copy Detection Techniques <s> Discriminability <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> BIB005
Discriminability is used to measure the performance of hashing algorithms when dissimilar and visually different images are compared to the original image.  BIB001 . Theoretically, the discrimination parameter should have a higher normalized mean & higher minimum and maximum difference values as compared to the robustness parameter. Table 8 gives the discrimination values for some of the most popular hashing techniques that have been reported in the literature. It can be seen from Table 8 that all the parameters for discrimination are much higher compared to robustness for the reviewed techniques listed in Table 7 . Table 7 . Robustness parameters generated by various reported techniques. BIB002 exhibits the best performance. It is important here to clarify that the range of robustness values should be lower than the range for discrimination. The larger the gap between them, better is the performance. A technique exhibiting such behavior is considered to be the most promising. However, these two ranges tend to overlap due to the presence of outliers in the analyzed data. To perform a more consistent analysis, we considered the second largest value for robustness in Table 7 (column 3). The second largest robustness values and the minimum discrimination values (Table 8) in [18, BIB003 BIB005 .  are 6, 5.46, 215, 7.32 and 6, 5.41, 204.9, 6.46, respectively. It is evident that these values only overlap slightly, which is acceptable. Among the reviewed techniques [18] returns the best performance as it is having 6 as its next highest value for robustness and minimum value for discrimination, which clearly shows the separation between robustness and discrimination. Similarly, the maximum (Max) difference values for robustness (Table 7) and discrimination (Table 8) for [18, BIB003  BIB005  BIB002 It can be seen that the maximum difference values of discrimination of most of the reviewed techniques are almost three times the maximum difference values of robustness except in BIB004 . This again serves as a good indicator for the performance of these algorithms. Based on the values given above, BIB002 again claims to exhibit the best performance.