Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks
This work addresses the problem of manual tuning in black-box adversarial attacks for machine learning security, offering a transferable solution that is incremental over existing methods.
The paper tackles the inefficiency of randomized search-based adversarial attacks by adapting their proposal distributions online, resulting in up to 20% improvement in black-box robustness estimates across various models and query regimes.
Adversarial attacks based on randomized search schemes have obtained state-of-the-art results in black-box robustness evaluation recently. However, as we demonstrate in this work, their efficiency in different query budget regimes depends on manual design and heuristic tuning of the underlying proposal distributions. We study how this issue can be addressed by adapting the proposal distribution online based on the information obtained during the attack. We consider Square Attack, which is a state-of-the-art score-based black-box attack, and demonstrate how its performance can be improved by a learned controller that adjusts the parameters of the proposal distribution online during the attack. We train the controller using gradient-based end-to-end training on a CIFAR10 model with white box access. We demonstrate that plugging the learned controller into the attack consistently improves its black-box robustness estimate in different query regimes by up to 20% for a wide range of different models with black-box access. We further show that the learned adaptation principle transfers well to the other data distributions such as CIFAR100 or ImageNet and to the targeted attack setting.