  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%.
