A Review of Hashing based Image Copy Detection Techniques <s> Robustness <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> BIB001 </s> A Review of Hashing based Image Copy Detection Techniques <s> Robustness <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> BIB002
Robustness of the hashing algorithm is evaluated to determine its capability to resist various types of image preprocessing attacks. A perfect image-hashing algorithm identifies all its preprocessed variants as multiple copies of the original image. Table  7 shows the robustness parameters generated by the reviewed hashing algorithms, which are obtained by calculating the difference between the hash values of original images and its duplicate copies. It includes parameters such as maximum, minimum, mean and standard deviation. The table also includes a column named as the next highest value and visual identification. The next highest value corresponds to the second highest maximum difference value between the original image and its preprocessed variant(s) while visual identification represents the percentage of visually identical images correctly identified as similar images. It is quite obvious that a lower mean along with a lower standard deviation implies that the difference between the hash values is small and they exist in the vicinity of the mean. Such an observation can lead to the deduction that various attacks performed on the original image have been largely ineffective and that the attacked images are very similar to the original image. To perform a fair comparison, a normalized mean and normalized standard deviation is employed which is shown in Table 7 . Normalized values are obtained by dividing the mean and standard deviation of the techniques with their threshold value. From Table 7 , it is clear that BIB002 has the lowest normalized mean along with a low value of normalized standard deviation. This indicates that the difference between the hash values of the original image and its attacked version is small and thus we can say that technique reported in BIB002 is resilient to most of the image processing attacks. In contrast, the technique in BIB001 returns a large value of the normalized mean, which implies that the images underwent significant changes as a result of various attacks. Also, the mechanism which returns a higher percentage of true positives and a low percentage of false positives considered ideal. The visual identification parameters are calculated by selecting an appropriate threshold value. Based upon the values given for visual identification in Table 7 [24] again claims to produce the best performance.