Assessing Perceptual Hash Algorithms for Publicly Evaluatable Framework

Image manipulation threatens data integrity and public trust, making reliable authenticity tools essential. The development of a publicly evaluatable perceptual hash framework enables various applications, including private image search resilient to image alterations. Despite the potential of such a framework, little research has systematically analyzed the performance of various perceptual hash algorithms within it. In this paper, we assess the performance of several leading perceptual hash methods, including aHash, pHash, dHash, wHash, and DCT, across five diverse image datasets and examine how cryptographic techniques impact the effectiveness of the algorithms. Integrating advanced encryption techniques with perceptual hashing in this approach is instrumental in advancing data security. It improves the security, privacy, and computational efficiency of perceptual hashing, solidifying its importance within the overall methodology.