用注意力机制精准识别生物医学图像中的复制篡改区域
BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images
- 引入亲和力引导的自注意力与跨图像注意力模块
- 在基准数据集上检测准确率显著优于现有方法
- 适合生物医学图像真实性验证的研究者使用
我们提出 BioTamperNet，一种新型框架，用于检测篡改生物医学图像中的重复区域，其灵感来自状态空间模型（SSM）近似的亲和力引导注意力机制。现有取证模型主要在自然图像上训练，对生物医学数据中细微篡改的检测性能较差，可能影响实验有效性。为解决此问题，BioTamperNet引入亲和力引导的自注意力模块以捕捉图像内部相似性，以及亲和力引导的交叉注意力模块以建模跨图像对应关系。设计融合轻量级SSM启发的线性注意力机制，实现高效、细粒度定位。端到端训练下，BioTamperNet可同时识别篡改区域及其原始来源。在基准生物取证数据集上的大量实验表明，该方法在准确检测重复区域方面显著优于竞争基线。代码已开源：https://github.com/SoumyaroopNandi/BioTamperNet
原文摘要 · Abstract (English)
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
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