MADPHash: Manipulation-Aware Deep Perceptual Hashing using Feature Consistency

Perceptual hashing has garnered significant attention for its wide-ranging applications in image retrieval and authentication domains. However, existing algorithms often struggle to detect subtle manipulations confined to small regions of an image. In this paper, we introduce a novel framework, Manipulation-Aware Deep Perceptual Hashing (MADPHash), which leverages feature consistency to enhance sensitivity to such subtle manipulations. MADPHash explicitly treats tampered images as a distinct category, incorporates a tampering detection objective into the perceptual hash generation process, and employs a Consistency Constraint Module to amplify discrepancies between tampered and untampered regions. Comprehensive experiments conducted on five benchmark datasets demonstrate that MADPHash significantly improves the detection of subtle manipulations while maintaining robustness against content-preserving transformations, outperforming several state-of-the-art perceptual hashing methods.