Robust Image Hashing for Detecting Small Tampering Using a Hyperrectangular Region
Toshiki Itagaki, Yuki Funabiki, Toru Akishita · 2021
In this paper, we propose a robust image hashing method that enables detecting small tampering. Existing hashing methods are too robust, and the trade-off relation between the robustness and the sensitivity to visual content changes needs to be improved to detect small tampering. Though the adaptive thresholding method can improve the trade-off, there's more room to improve and it requires tampered image derived from the original, which limits its applications. To overcome these two drawbacks, we introduce a new concept of a hyperrectangular region in multi-dimensional hash space, which is determined at the timing of hash generation as the region that covers a hash cluster by using the maximum and the minimum of the cluster per each hash axis. We evaluate our method and the existing methods. Our method improves the trade-off, which achieves 0.9428 as AUC (Area Under the Curve) for detecting tampering that occupies about 0.1% area of the image in the presence of JPEG compression and reducing the size as content-preserving operations. Furthermore, our method does not require tampered image derived from the original, which differs from the existing method.