Deep fake technology paves the way for a new generation of super realistic artificial content. While this opens the door to extraordinary new applications, the malicious use of deepfakes allows for far more realistic disinformation attacks than ever before. In this paper, we start from the intuition that generating fake content introduces possible inconsistencies in the depth of the generated images. This extra information provides valuable spatial and semantic cues that can reveal inconsistencies facial generative methods introduce. To test this idea, we evaluate different strategies for integrating depth information into an RGB detector and we propose an attention mechanism that makes it possible to integrate information from depth effectively. In addition to being more accurate than an RGB model, our method is more robust against common adversarial attacks on average than a typical RGB detector. Furthermore, we show how this technique allows the model to learn more discriminative features than RGB alone.

中文翻译：

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基于引导的 Deepfake 检测方法：通过特征融合进行 RGB 深度集成

深度造假技术为新一代超现实人工内容铺平了道路。虽然这为非凡的新应用程序打开了大门，但恶意使用深度伪造品会导致比以往任何时候都更加真实的虚假信息攻击。在本文中，我们从直觉出发，生成虚假内容会导致生成图像的深度可能不一致。这些额外的信息提供了有价值的空间和语义线索，可以揭示面部生成方法引入的不一致之处。为了测试这个想法，我们评估了将深度信息集成到 RGB 检测器中的不同策略，并提出了一种注意力机制，可以有效地集成深度信息。除了比 RGB 模型更准确之外，我们的方法平均比典型的 RGB 检测器更能抵御常见的对抗性攻击。此外，我们还展示了这种技术如何让模型比单独的 RGB 学习更多的判别性特征。