发表机构
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对深度伪造检测的鲁棒性与可解释性缺陷，提出含特征鲁棒增强、证据支撑偏好优化的框架，在ACM Multimedia 2026相关任务中获第一名。
AI 中文摘要
解释性深度伪造检测在二元分类基础上提出了更高要求，模型不仅要预测真实性，还需提供可解释的依据，这在法医分析师等用户需要了解检测逻辑的实际场景中至关重要。尽管已有进展，现有方法仍存在两大关键缺陷：一是对图像质量下降的鲁棒性不足，低质量样本检测准确率骤降，而朴素增强策略可能引发特征漂移，且随多样性扩大损害性能；二是解释存在事实性缺陷，解释模型可能遗漏篡改证据或生成无关细节，削弱可解释性。为解决该问题，本文提出包含两项创新的框架：针对鲁棒深度伪造检测，引入特征鲁棒增强，涵盖多样化的感知退化增强策略，以及结合均值教师架构的监督对比学习模式，通过一致性约束稳定特征以应对增强操作；针对解释，设计证据支撑的偏好优化过程，通过学习选择-拒绝解释对引导模型优先关注真实篡改痕迹，其中拒绝样本通过遗漏证据或注入无关信息构建。该方法在ACM Multimedia 2026可解释深度伪造检测任务中获第一名，代码可在指定链接获取。
英文摘要
Explainable deepfake detection extends binary classification by requiring models to not only predict authenticity but also provide interpretable justifications. This expanded scope is critical in practice, where users like forensic analysts need insight into the rationale behind the detection. Despite advancements, current approaches suffer from two critical deficiencies: (1)vulnerability to image quality degradation: detection accuracy plummets on low-quality samples, while naive augmentation strategies may induce feature drift and impair performance as diversity expands. (2) factually flawed explanations: explanation models may omit manipulation evidence or hallucinate irrelevant details, undermining interpretability. To address it, we propose a framework with two innovations. For robust deepfake detection, we introduce Feature-robust Augmentation, which comprises diversified degradation-aware augmentation strategies, and a supervised contrastive learning pattern paired with a mean-teacher architecture that stabilizes features against augmentations through consistency constraints. For explanation, we devise an evidence-grounded preference optimization process that guides model to prioritize genuine manipulation traces by learning from chosen-rejected explanation pairs, where rejected samples are constructed via evidence omission or irrelevant information injection. The proposed approach wins the first place in ACM Multimedia 2026 Explainable Deepfake Detection Challenge.The code is available at https://github.com/oceanflowlab/EDD.git.
