BioTamperNet：基于状态空间模型的亲和引导检测篡改生物医学图像
BioTamperNet: Affinity-Guided State-Space Model Detecting Tampered Biomedical Images
AI总结：
BioTamperNet通过亲和引导的注意力机制，高效检测生物医学图像中的篡改区域，提升检测精度。
摘要：
我们提出BioTamperNet，一种用于检测篡改生物医学图像中重复区域的新框架，利用受状态空间模型（SSM）近似启发的亲和引导注意力机制。现有取证模型主要在自然图像上训练，往往在生物医学数据上表现不佳，因为细微的篡改可能会影响实验的有效性。为了解决这个问题，BioTamperNet引入了亲和引导的自注意力模块以捕捉图像内的相似性，以及亲和引导的跨图像注意力模块以建模跨图像的对应关系。我们的设计集成了轻量级的SSM启发线性注意力机制，以实现高效、精细的定位。端到端训练的BioTamperNet能够同时识别篡改区域及其来源对应物。在基准生物取证数据集上的大量实验表明，BioTamperNet在准确检测重复区域方面显著优于竞争基线。代码 - https://github.com/SoumyaroopNandi/BioTamperNet
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
