论文标题
基于面部及其背景之间的差异的深泡检测
DeepFake Detection Based on the Discrepancy Between the Face and its Context
论文作者
论文摘要
我们提出了一种检测单个图像中面部交换和其他身份操作的方法。面部交换方法（例如Deepfake）操纵面部区域，旨在调整面部的外观外观，同时使背景不变。我们表明，这种作案手法在两个区域之间产生差异。这些差异提供了可剥削的操纵迹象。我们的方法涉及两个网络：（i）一个面部识别网络，该网络认为面部区域受到紧密的语义分割的界定，以及（ii）考虑面部上下文（例如，头发，耳朵，颈部）的上下文识别网络。我们描述了一种使用来自我们两个网络的识别信号来检测此类差异的方法，提供了一个互补的检测信号，该信号改善了常规的真实和假的分类器，通常用于检测假图像。我们的方法在面部福音++，celeb-df-v2和DFDC基准测试中实现了最新的结果，以进行面部操纵检测，甚至概括以检测未见方法产生的假货。
We propose a method for detecting face swapping and other identity manipulations in single images. Face swapping methods, such as DeepFake, manipulate the face region, aiming to adjust the face to the appearance of its context, while leaving the context unchanged. We show that this modus operandi produces discrepancies between the two regions. These discrepancies offer exploitable telltale signs of manipulation. Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context (e.g., hair, ears, neck). We describe a method which uses the recognition signals from our two networks to detect such discrepancies, providing a complementary detection signal that improves conventional real vs. fake classifiers commonly used for detecting fake images. Our method achieves state of the art results on the FaceForensics++, Celeb-DF-v2, and DFDC benchmarks for face manipulation detection, and even generalizes to detect fakes produced by unseen methods.
