With the proliferation of face image manipulation (FIM) techniques such as Face2Face and Deepfake, more fake face images are spreading over the internet, which brings serious challenges to public confidence. Face image forgery detection has made considerable progresses in exposing specific FIM, but it is still in scarcity of a robust fake face detector to expose face image forgeries under complex scenarios such as with further compression, blurring, scaling, etc. Due to the relatively fixed structure, convolutional neural network (CNN) tends to learn image content representations. However, CNN should learn subtle manipulation traces for image forensics tasks. Thus, we propose an adaptive manipulation traces extraction network (AMTEN), which serves as pre-processing to suppress image content and highlight manipulation traces. AMTEN exploits an adaptive convolution layer to predict manipulation traces in the image, which are reused in subsequent layers to maximize manipulation artifacts by updating weights during the back-propagation pass. A fake face detector, namely AMTENnet, is constructed by integrating AMTEN with CNN. Experimental results prove that the proposed AMTEN achieves desirable pre-processing. When detecting fake face images generated by various FIM techniques, AMTENnet achieves an average accuracy up to 98.52%, which outperforms the state-of-the-art works. When detecting face images with unknown post-processing operations, the detector also achieves an average accuracy of 95.17%.

随着诸如Face2Face和Deepfake之类的面部图像处理（FIM）技术的普及，越来越多的假面部图像通过Internet传播，这给公众信心带来了严峻挑战。人脸图像伪造检测在公开特定的FIM方面已经取得了长足的进步，但是仍然缺乏鲁棒的假人脸检测器在复杂的情况下（例如进一步压缩，模糊，缩放等）暴露人脸图像伪造。由于相对固定在结构上，卷积神经网络（CNN）倾向于学习图像内容表示。但是，CNN应该学习图像取证任务的细微操作痕迹。因此，我们提出了一种自适应操纵痕迹提取网络（AMTEN），该网络可作为预处理来抑制图像内容并突出显示操纵痕迹。AMTEN利用自适应卷积层来预测图像中的操纵轨迹，然后在后续层中通过在反向传播过程中更新权重将其重用于最大化操纵伪像。通过将AMTEN与CNN集成在一起，构建了一个伪造的面部检测器，即AMTENnet。实验结果证明，提出的AMTEN可以实现理想的预处理。当检测由各种FIM技术生成的假人脸图像时，AMTENnet的平均准确率高达98.52％，优于最新技术。当以未知的后处理操作检测人脸图像时，该检测器的平均准确度也达到了95.17％。通过在反向传播过程中更新权重，可在随后的层中重用它们以最大化操纵伪像。通过将AMTEN与CNN集成在一起，构建了一个伪造的面部检测器，即AMTENnet。实验结果证明，提出的AMTEN可以实现理想的预处理。当检测由各种FIM技术生成的假人脸图像时，AMTENnet的平均准确率高达98.52％，优于最新技术。当以未知的后处理操作检测人脸图像时，该检测器的平均准确度也达到了95.17％。通过在反向传播过程中更新权重，可在随后的层中重用它们以最大化操纵伪像。通过将AMTEN与CNN集成在一起，构建了一个伪造的面部检测器，即AMTENnet。实验结果证明，提出的AMTEN可以实现理想的预处理。当检测由各种FIM技术生成的假人脸图像时，AMTENnet的平均准确率高达98.52％，优于最新技术。当以未知的后处理操作检测人脸图像时，该检测器的平均准确度也达到了95.17％。AMTENnet的平均准确率高达98.52％，优于最新技术。当以未知的后处理操作检测人脸图像时，该检测器的平均准确度也达到了95.17％。AMTENnet的平均准确率高达98.52％，优于最新技术。当以未知的后处理操作检测人脸图像时，该检测器的平均准确度也达到了95.17％。