Due to the successful development of deep image generation technology, visual data forgery detection would play a more important role in social and economic security. Existing forgery detection methods suffer from unsatisfactory generalization ability to determine the authenticity in the unseen domain. In this paper, we propose a novel Attention Consistency Refined masked frequency forgery representation model toward generalizing face forgery detection algorithm (ACMF). Most forgery technologies always bring in high-frequency aware cues, which make it easy to distinguish source authenticity but difficult to generalize to unseen artifact types. The masked frequency forgery representation module is designed to explore robust forgery cues by randomly discarding high-frequency information. In addition, we find that the forgery attention map inconsistency through the detection network could affect the generalizability. Thus, the forgery attention consistency is introduced to force detectors to focus on similar attention regions for better generalization ability. Experiment results on several public face forgery datasets (FaceForensic++, DFD, Celeb-DF, and WDF datasets) demonstrate the superior performance of the proposed method compared with the state-of-the-art methods.

翻译：由于深度图像生成技术的成功发展，视觉数据伪造检测将在社会经济安全中发挥更重要作用。现有伪造检测方法在判定未知域真实性时存在泛化能力不足的问题。本文提出一种新颖的注意力一致性精炼掩码频率伪造表示模型，用于泛化人脸伪造检测算法（ACMF）。多数伪造技术总会引入高频感知线索，这使得区分来源真实性较为容易，但难以泛化到未见过的伪造类型。通过随机丢弃高频信息，掩码频率伪造表示模块旨在探索鲁棒的伪造线索。此外，我们发现检测网络中的伪造注意力图不一致性会影响泛化能力。因此，引入伪造注意力一致性，迫使检测器聚焦于相似的注意力区域，以获得更好的泛化能力。在多个公开人脸伪造数据集（FaceForensic++、DFD、Celeb-DF和WDF数据集）上的实验结果表明，与现有最先进方法相比，所提方法具有更优越的性能。