DF-CBM：用于深度伪造检测的区域感知概念瓶颈模型
DF-CBM: Region-Aware Concept Bottleneck Models for Deepfake Detection
- Kingston University London（伦敦金斯顿大学）
- Centre for Research and Technology Hellas (CERTH)（希腊研究与技术中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出DF-CBM，一种区域感知概念瓶颈模型，通过概念预测与掩蔽注意力实现可解释的深度伪造检测，优于概念基线并接近黑盒检测器性能。
AI中文摘要：
深度伪造检测方法已变得越来越有效，然而大多数方法对其预测背后的证据提供的洞察有限。然而，在取证环境中，用户还需要知道哪些操纵线索支持决策以及它们出现在何处。现有的可解释性方法仅部分满足这一需求，因为基于定位的方法缺乏语义描述，而基于语言的解释方法在视觉证据上的基础较弱。在这项工作中，我们提出了DF-CBM，一种用于可解释深度伪造检测的区域感知概念瓶颈模型。DF-CBM从文本伪影标注中构建一个紧凑的与操纵相关的概念词汇表，并将每个概念与合理的面部和边界区域相关联。然后，它使用由解析的面部掩码引导的概念特定掩蔽注意力机制从视觉特征中预测这些概念，最终的实/假决策由预测的概念瓶颈做出。我们的实验表明，DF-CBM在概念预测和深度伪造分类方面优于基于概念的基线，同时与最先进的黑盒检测器保持竞争力。最后，定性结果和干预分析表明，DF-CBM提供了空间基础的概念证据，并能够对个体操纵概念如何影响最终预测进行反事实解释。我们的代码可在以下网址获取：this https URL。
英文摘要：
Deepfake detection methods have become increasingly effective yet most provide limited insight into the evidence behind their predictions. However, in forensic settings users also need to know which manipulation cues support the decision and where they appear. Existing explainability methods only partially address this need since localization-based approaches lack semantic descriptions while language-based explanation methods are only weakly grounded in visual evidence. In this work, we propose DF-CBM, a region-aware concept bottleneck model for explainable deepfake detection. DF-CBM builds a compact vocabulary of manipulation-related concepts from textual artifact annotations and links each concept to plausible facial and boundary regions. It then predicts these concepts from visual features using a concept-specific masked attention mechanism guided by parsed facial masks and the final real/fake decision is made from the predicted concept bottleneck. Our experiments show that DF-CBM outperforms concept-based baselines in concept prediction and deepfake classification while remaining competitive with state-of-the-art black-box detectors. Finally, qualitative results and intervention analyses demonstrate that DF-CBM provides spatially grounded concept evidence and enables counterfactual explanations of how individual manipulation concepts influence the final prediction. Our code is available at: https://github.com/GeorgeTsoumplekas/DF-CBM.
