Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks
This addresses the vulnerability of deep learning models to unpredictable adversarial attacks, offering a practical solution for improving robustness, though it is incremental as it builds on existing evolutionary and adversarial defense methods.
The paper tackles the problem of designing neural network architectures robust to multiple adversarial attacks by proposing a multi-objective evolutionary search method, resulting in optimized architectures that outperform state-of-the-art networks in classification accuracy under various attacks.
Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans. To address this issue, various methods have been proposed to design network architectures that are robust to one particular type of adversarial attacks. It is practically impossible, however, to predict beforehand which type of attacks a machine learn model may suffer from. To address this challenge, we propose to search for deep neural architectures that are robust to five types of well-known adversarial attacks using a multi-objective evolutionary algorithm. To reduce the computational cost, a normalized error rate of a randomly chosen attack is calculated as the robustness for each newly generated neural architecture at each generation. All non-dominated network architectures obtained by the proposed method are then fully trained against randomly chosen adversarial attacks and tested on two widely used datasets. Our experimental results demonstrate the superiority of optimized neural architectures found by the proposed approach over state-of-the-art networks that are widely used in the literature in terms of the classification accuracy under different adversarial attacks.