  Camera trace is a unique noise produced in digital imaging process. Most
existing forensic methods analyze camera trace to identify image origins. In
this paper, we address a new low-level vision problem, camera trace erasing, to
reveal the weakness of trace-based forensic methods. A comprehensive
investigation on existing anti-forensic methods reveals that it is non-trivial
to effectively erase camera trace while avoiding the destruction of content
signal. To reconcile these two demands, we propose Siamese Trace Erasing
(SiamTE), in which a novel hybrid loss is designed on the basis of Siamese
architecture for network training. Specifically, we propose embedded
similarity, truncated fidelity, and cross identity to form the hybrid loss.
Compared with existing anti-forensic methods, SiamTE has a clear advantage for
camera trace erasing, which is demonstrated in three representative tasks. Code
and dataset are available at https://github.com/ngchc/CameraTE.
