Maintaining Adversarial Robustness in Continuous Learning
Xiaolei Ru Xiaowei Cao Zijia Liu Jack Murdoch Moore Xin-Ya Zhang Xia Zhu Wenjia Wei Gang Yan
Adversarial robustness is essential for security and reliability of machine
learning systems. However, the adversarial robustness gained by sophisticated
defense algorithms is easily erased as the neural network evolves to learn new
tasks. This vulnerability can be addressed by fostering a novel capability for
neural networks, termed continual robust learning, which focuses on both the
(classification) performance and adversarial robustness on previous tasks
during continuous learning. To achieve continuous robust learning, we propose
an approach called Double Gradient Projection that projects the gradients for
weight updates orthogonally onto two crucial subspaces -- one for stabilizing
the smoothed sample gradients and another for stabilizing the final outputs of
the neural network. The experimental results on four benchmarks demonstrate
that the proposed approach effectively maintains continuous robustness against
strong adversarial attacks, outperforming the baselines formed by combining the
existing defense strategies and continual learning methods.