  We introduce an adversarial sample detection algorithm based on image
residuals, specifically designed to guard against patch-based attacks. The
image residual is obtained as the difference between an input image and a
denoised version of it, and a discriminator is trained to distinguish between
clean and adversarial samples. More precisely, we use a wavelet domain
algorithm for denoising images and demonstrate that the obtained residuals act
as a digital fingerprint for adversarial attacks. To emulate the limitations of
a physical adversary, we evaluate the performance of our approach against
localized (patch-based) adversarial attacks, including in settings where the
adversary has complete knowledge about the detection scheme. Our results show
that the proposed detection method generalizes to previously unseen, stronger
attacks and that it is able to reduce the success rate (conversely, increase
the computational effort) of an adaptive attacker.
