The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken essential steps on GAN-generated faces, more threatening attacks related to identity swapping or expression transferring are still overlooked. And the forgery traces hidden in unknown attacks from the open-world unlabeled faces still remain under-explored. To push the related frontier research, we introduce a new benchmark called Open-World DeepFake Attribution (OW-DFA), which aims to evaluate attribution performance against various types of fake faces under open-world scenarios. Meanwhile, we propose a novel framework named Contrastive Pseudo Learning (CPL) for the OW-DFA task through 1) introducing a Global-Local Voting module to guide the feature alignment of forged faces with different manipulated regions, 2) designing a Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused by similar methods in unlabeled set. In addition, we extend the CPL framework with a multi-stage paradigm that leverages pre-train technique and iterative learning to further enhance traceability performance. Extensive experiments verify the superiority of our proposed method on the OW-DFA and also demonstrate the interpretability of deepfake attribution task and its impact on improving the security of deepfake detection area.

翻译：随着生成技术的快速发展，伪造人脸溯源问题已引起广泛关注。尽管近期多项研究在GAN生成人脸方面取得了重要进展，但与身份交换或表情迁移相关的更具威胁性的攻击手段仍被忽视。此外，开放世界中未经标注的人脸图像所隐藏的未知攻击伪造痕迹仍未得到充分探索。为推动这一前沿领域研究，我们引入名为"开放世界深度伪造溯源"（OW-DFA）的新基准，旨在评估开放场景下针对各类伪造人脸的溯源性能。同时，我们提出名为对比伪学习（CPL）的新型框架以解决OW-DFA任务，其核心创新包括：1）引入全局-局部投票模块，引导不同操纵区域伪造人脸的特征对齐；2）设计基于置信度的软伪标签策略，缓解未标注数据集中相似方法导致的伪噪声。此外，我们通过多阶段范式扩展CPL框架，利用预训练技术和迭代学习进一步增强溯源能力。大量实验验证了所提方法在OW-DFA上的优越性，同时揭示了深度伪造溯源任务的可解释性及其对提升深度伪造检测领域安全性的重要影响。