发现深度伪造检测模型依赖低层合成痕迹，而非语义异常。
The Alpha Blending Hypothesis: Compositing Shortcut in Deepfake Detection
- 检测模型本质是寻找图像拼接时的透明度混合特征。
- 在15个数据集上实现94.0%的最高跨数据集准确率。
- 适合研究伪造检测机制或提升模型鲁棒性的学者。
近期深度伪造检测方法虽具备良好跨数据集泛化能力，但其内在机制仍不明确。本文提出Alpha混合假说，认为先进帧级检测器主要充当透明度混合搜索器——它们并非学习语义异常或生成模型指纹，而是定位伪造人脸嵌入目标帧时产生的低层合成痕迹。实验验证该假说：检测模型对自混合图像（SBI）和非生成性篡改高度敏感。为此提出BlenD方法，利用大规模真实人脸数据集与SBI增强，无需训练时使用显式生成的伪造图像，在2019至2025年间发布的15个组合型深度伪造数据集上取得最佳平均跨数据集泛化性能。此外，显式混合搜索器与抗混合捷径模型预测高度互补，集成后达到94.0%的AUROC，为当前最优结果。代码与训练模型将公开发布。
原文摘要 · Abstract (English)
Recent deepfake detection methods demonstrate improved cross-dataset generalization, yet the underlying mechanisms remain underexplored. We introduce the Alpha Blending Hypothesis, positing that state-of-the-art frame-based detectors primarily function as alpha blending searchers; rather than learning semantic anomalies or specific generative neural fingerprints, they localize low-level compositing artifacts introduced during the integration of manipulated faces into target frames. We experimentally validate the hypothesis, demonstrating that deepfake detectors exhibit high sensitivity to the so-called self-blended images (SBI) and non-generative manipulations. We propose the method BlenD that leverages a large-scale, diverse dataset of real-only facial images augmented with SBI. This approach achieves the best average cross-dataset generalization on 15 compositional deepfake datasets released between 2019 and 2025 without utilizing explicitly generated deepfakes during training. Furthermore, we show that predictions from explicit blending searchers and models resilient to blending shortcuts are highly complementary, yielding a state-of-the-art AUROC of 94.0% in an ensemble configuration. The code with experiments and the trained model will be publicly released.
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