BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images

2026-02-01

AI Summary

This work addresses the challenge that existing image forensic methods, trained primarily on natural images, struggle to effectively detect subtle copy-paste manipulations in biomedical images, thereby compromising the integrity of scientific data. To this end, we propose BioTamperNet, a novel framework that, for the first time, integrates affinity-guided self-attention and cross-attention mechanisms with a lightweight linear attention module inspired by state space models. This architecture enables end-to-end precise localization of both tampered regions and their corresponding source regions. Evaluated on standard biomedical image forensics benchmarks, BioTamperNet significantly outperforms current state-of-the-art methods, demonstrating superior accuracy and fine-grained detection capability.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Bio-inspired Learning
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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

Problem

• biomedical image tampering
• image forgery detection
• duplicated region detection
• bio-forensics
• medical image integrity

Innovation

• BioTamperNet
• affinity-guided attention
• State Space Model
• biomedical image forensics
• copy-move detection