HaMP: a proactive hash-based framework for manipulation localization

Abstract With the continuous advancement of image tampering techniques, traditional hash-based tampering detection algorithms face numerous challenges, particularly in handling complex tampering types and achieving object-level precise localization. Existing methods often rely on manually designed features, resulting in forensic hashes that lack sufficient detail and fail to capture the fine-grained information needed for accurate localization. To address these limitations, HaMP, a novel hash-based proactive tampering localization method, is proposed. HaMP introduces a Hash Construction Module (HCM) capable of generating compact yet information-rich image hashes. By integrating multi-scale feature extraction, the HCM produces robust global representations of the image. For precise tampering localization, the proposed Localization Module (LM) refines these representations by leveraging hash differences and multi-scale features. This enables accurate detection of tampered regions, even in the presence of subtle modifications. Experimental results demonstrate that HaMP significantly outperforms traditional methods, showcasing enhanced robustness, accuracy, and generalization across multiple datasets.