Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning
This work addresses security risks in machine learning applications by improving adversarial attacks for scenarios where model internals are inaccessible, offering incremental advancements in decision-based attack methods.
The paper tackles the problem of generating adversarial perturbations in decision-based black-box settings, where only model output labels are available, by proposing a reinforcement learning method called DBAR that achieves higher attack success rates and greater transferability compared to state-of-the-art approaches.
The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. Research on adversarial attacks is beneficial in guiding the development of machine learning models on the one hand and finding targeted defenses on the other. However, most of the adversarial attacks today leverage the gradient or logit information from the models to generate adversarial perturbation. Works in the more realistic domain: decision-based attacks, which generate adversarial perturbation solely based on observing the output label of the targeted model, are still relatively rare and mostly use gradient-estimation strategies. In this work, we propose a pixel-wise decision-based attack algorithm that finds a distribution of adversarial perturbation through a reinforcement learning algorithm. We call this method Decision-based Black-box Attack with Reinforcement learning (DBAR). Experiments show that the proposed approach outperforms state-of-the-art decision-based attacks with a higher attack success rate and greater transferability.