First published: 2018/09/24 (2 years ago) Abstract: Adversarial images aim to change a target model's decision by minimally
perturbing a target image. In the black-box setting, the absence of gradient
information often renders this search problem costly in terms of query
complexity. In this paper we propose to restrict the search for adversarial
images to a low frequency domain. This approach is readily compatible with many
existing black-box attack frameworks and consistently reduces their query cost
by 2 to 4 times. Further, we can circumvent image transformation defenses even
when both the model and the defense strategy are unknown. Finally, we
demonstrate the efficacy of this technique by fooling the Google Cloud Vision
platform with an unprecedented low number of model queries.
Guo et al. propose to augment black-box adversarial attacks with low-frequency noise to obtain low-frequency adversarial examples as shown in Figure 1. To this end, the boundary attack as well as the NES attack are modified to sample from a low-frequency Gaussian distribution instead from Gaussian noise directly. This is achieved through an inverse discrete cosine transform as detailed in the paper.
Figure 1: Example of a low-frequency adversarial example.
Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).