  Transferability of adversarial examples is a key issue to apply this kind of
attacks against multimedia forensics (MMF) techniques based on Deep Learning
(DL) in a real-life setting. Adversarial example transferability, in fact,
would open the way to the deployment of successful counter forensics attacks
also in cases where the attacker does not have a full knowledge of the
to-be-attacked system. Some preliminary works have shown that adversarial
examples against CNN-based image forensics detectors are in general
non-transferrable, at least when the basic versions of the attacks implemented
in the most popular libraries are adopted. In this paper, we introduce a
general strategy to increase the strength of the attacks and evaluate their
transferability when such a strength varies. We experimentally show that, in
this way, attack transferability can be largely increased, at the expense of a
larger distortion. Our research confirms the security threats posed by the
existence of adversarial examples even in multimedia forensics scenarios, thus
calling for new defense strategies to improve the security of DL-based MMF
techniques.
