In this paper, we propose a new DeepFakes forensics approach called forensic symmetry, which determines whether two symmetrical face patches contain the same or different natural features. To do this, we propose a multi-stream learning structure composed of two feature extractors. The first feature extractor obtains symmetry feature from the front face images. The second feature extractor obtains similarity feature from the side face images. Symmetry feature and similarity feature are collectively called natural feature. Forensic symmetry system maps the pair of symmetrical face patches into the angular hyperspace to quantify the difference of their natural features. The greater the difference of natural features, the higher the tamper probability of face images. The heuristic prediction algorithm is designed to compute the tamper probability of DeepFakes at video level. A series of experiments are carried out to evaluate the effectiveness of our proposed forensic symmetry system. Experimental results show that our approach is effective for DeepFakes detection under the scenarios of homologous detection, heterogeneous detection, and re- compression detection.

中文翻译：

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DeepFakes 的法医对称性


在本文中，我们提出了一种新的 DeepFakes 取证方法，称为取证对称，它确定两个对称的面部补丁是否包含相同或不同的自然特征。为此，我们提出了一种由两个特征提取器组成的多流学习结构。第一个特征提取器从正面图像中获取对称特征。第二特征提取器从侧面图像获得相似特征。对称特征和相似特征统称为自然特征。法医对称系统将一对对称的面部斑块映射到角度超空间中，以量化其自然特征的差异。自然特征差异越大，人脸图像的篡改概率就越高。启发式预测算法旨在计算视频级别的 DeepFakes 篡改概率。进行了一系列实验来评估我们提出的法医对称系统的有效性。实验结果表明，我们的方法在同源检测、异构检测和重新压缩检测场景下对 DeepFakes 检测是有效的。