Reinforcement Learning Platform for Adversarial Black-box Attacks with Custom Distortion Filters
Journal:
arXiv
Published Date:
Jan 23, 2025
Abstract
We present a Reinforcement Learning Platform for Adversarial Black-box
untargeted and targeted attacks, RLAB, that allows users to select from various
distortion filters to create adversarial examples. The platform uses a
Reinforcement Learning agent to add minimum distortion to input images while
still causing misclassification by the target model. The agent uses a novel
dual-action method to explore the input image at each step to identify
sensitive regions for adding distortions while removing noises that have less
impact on the target model. This dual action leads to faster and more efficient
convergence of the attack. The platform can also be used to measure the
robustness of image classification models against specific distortion types.
Also, retraining the model with adversarial samples significantly improved
robustness when evaluated on benchmark datasets. The proposed platform
outperforms state-of-the-art methods in terms of the average number of queries
required to cause misclassification. This advances trustworthiness with a
positive social impact.
