  Anti-forensics seeks to eliminate or conceal traces of tampering artifacts.
Typically, anti-forensic methods are designed to deceive binary detectors and
persuade them to misjudge the authenticity of an image. However, to the best of
our knowledge, no attempts have been made to deceive forgery detectors at the
pixel level and mis-locate forged regions. Traditional adversarial attack
methods cannot be directly used against forgery localization due to the
following defects: 1) they tend to just naively induce the target forensic
models to flip their pixel-level pristine or forged decisions; 2) their
anti-forensics performance tends to be severely degraded when faced with the
unseen forensic models; 3) they lose validity once the target forensic models
are retrained with the anti-forensics images generated by them. To tackle the
three defects, we propose SEAR (Self-supErvised Anti-foRensics), a novel
self-supervised and adversarial training algorithm that effectively trains
deep-learning anti-forensic models against forgery localization. SEAR sets a
pretext task to reconstruct perturbation for self-supervised learning. In
adversarial training, SEAR employs a forgery localization model as a supervisor
to explore tampering features and constructs a deep-learning concealer to erase
corresponding traces. We have conducted largescale experiments across diverse
datasets. The experimental results demonstrate that, through the combination of
self-supervised learning and adversarial learning, SEAR successfully deceives
the state-of-the-art forgery localization methods, as well as tackle the three
defects regarding traditional adversarial attack methods mentioned above.
