BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images
This addresses the need for reliable forensic tools in biomedical research to prevent compromised experimental validity due to image tampering, representing a domain-specific incremental advance.
The paper tackles the problem of detecting duplicated regions in tampered biomedical images, where existing models trained on natural images often underperform, and reports significant improvements over competitive baselines on benchmark bio-forensic datasets.
We propose BioTamperNet, a novel framework for detecting duplicated regions in tampered biomedical images, leveraging affinity-guided attention inspired by State Space Model (SSM) approximations. Existing forensic models, primarily trained on natural images, often underperform on biomedical data where subtle manipulations can compromise experimental validity. To address this, BioTamperNet introduces an affinity-guided self-attention module to capture intra-image similarities and an affinity-guided cross-attention module to model cross-image correspondences. Our design integrates lightweight SSM-inspired linear attention mechanisms to enable efficient, fine-grained localization. Trained end-to-end, BioTamperNet simultaneously identifies tampered regions and their source counterparts. Extensive experiments on the benchmark bio-forensic datasets demonstrate significant improvements over competitive baselines in accurately detecting duplicated regions. Code - https://github.com/SoumyaroopNandi/BioTamperNet