Robust hash generation technique for content-based image authentication using histogram

Abstract

In this paper, a robust hash technique for image content authentication using histogram is proposed. The histogram based hash techniques reported in the literature are robust against Content Preserving Manipulations as well as incidental distortion. The major drawback of these techniques is that, they are not sensitive to Content Changing Manipulations and also un-altered histogram image modifications. To overcome these drawbacks, we present a novel hash technique which divides the image into non-overlapped blocks and distributes histogram bins of the image block into larger containers based on the Partial Sum of pixel count of histogram bins. An intermediate hash is produced by computing the ratio of pixel count between two neighbouring containers. The intermediate image hash is obtained by concatenating intermediate hashes of image blocks. Finally, the intermediate image hash is normalized and randomly permuted with a secret key to produce a robust and secure hash. The results shows that, the proposed method performs better when compared to the existing methods against the Content Preserving manipulations. Besides, the proposed method is more sensitive to Content Changing manipulations as well as un-altered histogram image modifications. The performance results on image authentication indicate that, the proposed method has high discriminative capability and strong robustness.

Robust image hashing based on structural and perceptual features for authentication of color images

“…Another similar approach is presented in [23], a perceptual hashing scheme for color images with hybrid feature extraction mechanism is proposed which generates shorter hash lengths than [14] with slightly lower AUC but the limitation is the much lengthy hybrid hashing steps which make the computational time very high. A robust image hashing method based on partial sum of histogram bins is elaborated in [24] as histogram hashing.…”

“…Area under ROC curve (AUC) is a measure of the efficiency of ROC. Table 5 illustrates the comparison of AUC and hash length of proposed method with some latest schemes [14,[23][24][25]. We provided the hash length in both binary bits and decimal digits for K = 21 .…”

“…Therefore it is evident that the proposed method is an efficient image hashing method with a high true positive rate and a very low false positive rate. [14] 0.9955 400 bits or 121 digits Hybrid hashing [23] 0.9947 343 bits or 104 digits Histogram hashing [24] 0.9572 448 bits or 135 digits MDS hashing [25] 0.9905 720 bits or 217 digits…”

“…Computational cost of the proposed color image hashing algorithm is evaluated using MATLAB 2017 on a machine having core i3-4030U CPU at 1.9GHz. Results are compared with [14,[23][24][25] and found better among compared methods because of the limitations discussed in Section 1. Table 6 shows a comparison of average computational time and the hash length.…”

General Multimedia Trust Authentication Framework for 5G Networks

“…The robustness of the hashes means that hashes remain the same or change a little after the perceptual content preserving operations are being conducted. For example, Lokanadham Naidu Vadlamudi et al [20] proposed a robust hash algorithm by using features of histogram for image authentication. Due to the robustness and sensitivity of this kind of method, it is much superior to the other three methods when it comes to the network packet loss phenomenon in 5G networks.…”