新伪造类型出现时也能识别，提升真实场景下人脸伪造检测能力
Open Set Face Forgery Detection via Dual-Level Evidence Collection
- 从空间与频域双维度提取特征证据，评估预测不确定性
- 对未知伪造类别检测准确率比现有方法高20%以上
- 适合应对不断涌现的新伪造技术，实用性强
人脸伪造内容的激增正严重削弱在线内容的真实性可信度。随着生成算法快速演进，新型伪造手法将持续出现，严重挑战现有检测方法。尽管当前人脸伪造检测技术已有进步，但大多局限于二分类（真实 vs. 假）或已知伪造类别的识别，无法发现全新伪造方式。本文研究开放集人脸伪造检测（OSFFD）问题，要求模型能识别未见过的伪造类别。为增强实际应用性，我们通过不确定性估计重新定义该问题，并提出双层级证据收集（DLED）方法：在空间和频域层面提取并融合类别特异性证据以估计预测不确定性。大量实验表明，DLED在多种设置下均达到领先性能，尤其在识别新型伪造类别时平均超越基线模型 20%。同时，其在标准二分类任务上也表现优异。
原文摘要 · Abstract (English)
The surge in face forgeries has increasingly undermined confidence in the authenticity of online content. As generation algorithms rapidly evolve, new fake categories will constantly emerge, severely challenging existing face forgery detection methods. Although face forgery detection has recently improved, current techniques remain largely confined to binary Real-vs-Fake classification or the recognition of known fake categories. Moreover, they fail to identify the emergence of entirely new forgery methods. In this work, we study the Open Set Face Forgery Detection (OSFFD) problem, which requires the detection model to identify novel fake categories. To enhance its real-world applicability, we reformulate the OSFFD problem and address it through uncertainty estimation. Specifically, we propose the Dual-Level Evidential face forgery Detection (DLED) approach, which estimates prediction uncertainty by extracting and integrating category-specific evidence on the spatial and frequency levels. Comprehensive experiments across diverse settings demonstrate that our proposed DLED approach achieves state-of-the-art performance. Notably, it surpasses various existing baseline models by a $20\%$ margin on average when identifying forgeries from novel fake categories. Concurrently, our DLED method yields competitive performance on the standard binary Real-versus-Fake face forgery detection task.
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