The emergence of deepfake technologies has become a matter of social concern as they pose threats to individual privacy and public security. It is now of great significance to develop reliable deepfake detectors. However, with numerous face manipulation algorithms present, it is almost impossible to collect sufficient representative fake faces, and it is hard for existing detectors to generalize to all types of manipulation. Therefore, we turn to learn the distribution of real faces, and indirectly identify fake images that deviate from the real face distribution. In this study, we propose Real Face Foundation Representation Learning (RFFR), which aims to learn a general representation from large-scale real face datasets and detect potential artifacts outside the distribution of RFFR. Specifically, we train a model on real face datasets by masked image modeling (MIM), which results in a discrepancy between input faces and the reconstructed ones when applying the model on fake samples. This discrepancy reveals the low-level artifacts not contained in RFFR, making it easier to build a deepfake detector sensitive to all kinds of potential artifacts outside the distribution of RFFR. Extensive experiments demonstrate that our method brings about better generalization performance, as it significantly outperforms the state-of-the-art methods in cross-manipulation evaluations, and has the potential to further improve by introducing extra real faces for training RFFR.
翻译:深假技术的出现已成为社会关注的一个问题,因为它们对个人隐私和公共安全构成威胁。现在,开发可靠的深假探测器非常重要。然而,随着许多面部操纵算法的出现,几乎不可能收集到足够的有代表性的假面孔,而现有的探测器很难将所有类型的操纵都概括起来。因此,我们转而了解真实面孔的分布,间接识别与真实面部分布不同的假图像。在本研究中,我们提议“真实面部基金会代表学习”(RFFR),目的是从大型真实面部数据集中学习一般代表,并探测RFFR发行之外的潜在文物。具体地说,我们用蒙面图像模型(MIM)对真实面部数据集进行了模型模型培训,这导致输入面部面孔与将模型应用到所有类型操纵上时的再版之间出现差异。这一差异揭示了RFFRFR没有包含的低层人工制品,因此更容易建立一种对各种潜在手工艺品具有敏感性的深雾体检测器。广泛的实验表明,我们的方法能够通过涂面图像模型进行更精确的演化,从而改进了真实性评估。</s>