Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks

Abstract

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.

References in corpus (9)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Neural Architecture Search with Reinforcement Learning
• Fast is better than free: Revisiting adversarial training
• Efficient Architecture Search by Network Transformation
• A study of the effect of JPG compression on adversarial images
• Defensive Distillation is Not Robust to Adversarial Examples
• On the Convergence and Robustness of Adversarial Training
• Towards Understanding Fast Adversarial Training
• An Introduction to Neural Architecture Search for Convolutional Networks