  For image forensics, convolutional neural networks (CNNs) tend to learn
content features rather than subtle manipulation traces, which limits forensic
performance. Existing methods predominantly solve the above challenges by
following a general pipeline, that is, subtracting the original pixel value
from the predicted pixel value to make CNNs pay attention to the manipulation
traces. However, due to the complicated learning mechanism, these methods may
bring some unnecessary performance losses. In this work, we rethink the
advantages of gradient operator in exposing face forgery, and design two
plug-and-play modules by combining gradient operator with CNNs, namely tensor
pre-processing (TP) and manipulation trace attention (MTA) module.
Specifically, TP module refines the feature tensor of each channel in the
network by gradient operator to highlight the manipulation traces and improve
the feature representation. Moreover, MTA module considers two dimensions,
namely channel and manipulation traces, to force the network to learn the
distribution of manipulation traces. These two modules can be seamlessly
integrated into CNNs for end-to-end training. Experiments show that the
proposed network achieves better results than prior works on five public
datasets. Especially, TP module greatly improves the accuracy by at least 4.60%
compared with the existing pre-processing module only via simple tensor
refinement. The code is available at:
https://github.com/EricGzq/GocNet-pytorch.
