  With the spread of tampered images, locating the tampered regions in digital
images has drawn increasing attention. The existing image tampering
localization methods, however, suffer from severe performance degradation when
the tampered images are subjected to some post-processing, as the tampering
traces would be distorted by the post-processing operations. The poor
robustness against post-processing has become a bottleneck for the practical
applications of image tampering localization techniques. In order to address
this issue, this paper proposes a novel restoration-assisted framework for
image tampering localization (ReLoc). The ReLoc framework mainly consists of an
image restoration module and a tampering localization module. The key idea of
ReLoc is to use the restoration module to recover a high-quality counterpart of
the distorted tampered image, such that the distorted tampering traces can be
re-enhanced, facilitating the tampering localization module to identify the
tampered regions. To achieve this, the restoration module is optimized not only
with the conventional constraints on image visual quality but also with a
forensics-oriented objective function. Furthermore, the restoration module and
the localization module are trained alternately, which can stabilize the
training process and is beneficial for improving the performance. The proposed
framework is evaluated by fighting against JPEG compression, the most commonly
used post-processing. Extensive experimental results show that ReLoc can
significantly improve the robustness against JPEG compression. The restoration
module in a well-trained ReLoc model is transferable. Namely, it is still
effective when being directly deployed with another tampering localization
module.
