新方法让人脸伪造检测更抗干扰，误报率更低。
Contrastive Desensitization Learning for Cross Domain Face Forgery Detection
- 通过对比去敏感化学习，提取跨域通用特征
- 在多个数据集上误报率显著降低，准确率提升
- 适合需要稳定检测的现实场景应用
本文提出一种新的跨域人脸伪造检测方法，对不同甚至未见过的伪造手法具有鲁棒性，同时保持较低的误报率。现有方法虽可在多领域适用，但常伴随高误报率，严重影响系统可用性。为此，我们设计了基于稳健去敏感化算法的对比去敏感化网络（CDN），通过成对真实人脸图像的域变换学习本质域特征。该方法所学表征在理论上可证明对域变化具有鲁棒性。大规模基准数据集上的实验表明，相比多种先进方法，本方法在保持更高检测准确率的同时，显著降低了误报率。
原文摘要 · Abstract (English)
In this paper, we propose a new cross-domain face forgery detection method that is insensitive to different and possibly unseen forgery methods while ensuring an acceptable low false positive rate. Although existing face forgery detection methods are applicable to multiple domains to some degree, they often come with a high false positive rate, which can greatly disrupt the usability of the system. To address this issue, we propose an Contrastive Desensitization Network (CDN) based on a robust desensitization algorithm, which captures the essential domain characteristics through learning them from domain transformation over pairs of genuine face images. One advantage of CDN lies in that the learnt face representation is theoretical justified with regard to the its robustness against the domain changes. Extensive experiments over large-scale benchmark datasets demonstrate that our method achieves a much lower false alarm rate with improved detection accuracy compared to several state-of-the-art methods.
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