  Deep neural networks provide unprecedented performance in all image
classification problems, taking advantage of huge amounts of data available for
training. Recent studies, however, have shown their vulnerability to
adversarial attacks, spawning an intense research effort in this field. With
the aim of building better systems, new countermeasures and stronger attacks
are proposed by the day. On the attacker's side, there is growing interest for
the realistic black-box scenario, in which the user has no access to the neural
network parameters. The problem is to design efficient attacks which mislead
the neural network without compromising image quality. In this work, we propose
to perform the black-box attack along a low-distortion path, so as to improve
both the attack efficiency and the perceptual quality of the adversarial image.
Numerical experiments on real-world systems prove the effectiveness of the
proposed approach, both in benchmark classification tasks and in key
applications in biometrics and forensics.
