Learning to Learn Transferable Attack
Keywords:Computer Vision (CV)
AbstractTransfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to the victim model. However, the transferability of perturbations from existing methods is still limited, since the adversarial perturbations are easily overﬁtting with a single surrogate model and speciﬁc data pattern. In this paper, we propose a Learning to Learn Transferable Attack (LLTA) method, which makes the adversarial perturbations more generalized via learning from both data and model augmentation. For data augmentation, we adopt simple random resizing and padding. For model augmentation, we randomly alter the back propagation instead of the forward propagation to eliminate the effect on the model prediction. By treating the attack of both speciﬁc data and a modiﬁed model as a task, we expect the adversarial perturbations to adopt enough tasks for generalization. To this end, the meta-learning algorithm is further introduced during the iteration of perturbation generation. Empirical results on the widely-used dataset demonstrate the effectiveness of our attack method with a 12.85% higher success rate of transfer attack compared with the state-of-the-art methods. We also evaluate our method on the real-world online system, i.e., Google Cloud Vision API, to further show the practical potentials of our method.
How to Cite
Fang, S., Li, J., Lin, X., & Ji, R. (2022). Learning to Learn Transferable Attack. Proceedings of the AAAI Conference on Artificial Intelligence, 36(1), 571-579. https://doi.org/10.1609/aaai.v36i1.19936
AAAI Technical Track on Computer Vision I