Title: Boosting Adversarial Training with Masked Adaptive Ensemble. Abstract: Adversarial training (AT) can help improve the robustness of a deep neural network (DNN) against potential adversarial attacks by intentionally injecting adversarial examples into the training data, but this way inevitably incurs standard accuracy degradation to some extent, thereby calling for a trade-off between standard accuracy and robustness. Besides, the prominent AT solutions are vulnerable to sparse attacks, due to “robustness overfitting” upon dense attacks, often adopted by AT to produce a threat model. To tackle such shortcomings, this paper proposes a novel framework, including a detector and a classifier bridged by our newly developed adaptive ensemble. Specifically, a Guided Backpropagation-based detector is designed to sniff adversarial examples, driven by our empirical observation. Meanwhile, a classifier with two encoders is employed for extracting visual representations respectively from clean images and adversarial examples. The adaptive ensemble approach also enables us to mask off a random subset of image patches within input data, eliminating potential adversarial effects when encountering malicious inputs with negligible standard accuracy degradation. As such, our approach enjoys improved robustness, able to withstand both dense and sparse attacks, while maintaining high standard accuracy. Experimental results exhibit that our detector and classifier outperform their state-of-the-art counterparts, in terms of detection accuracy, standard accuracy, and adversarial robustness. For example, on CIFAR-10, our detector achieves the best detection accuracy of 99.6% under dense attacks and of 98.5% under sparse attacks. Our classifier achieves the best standard accuracy of 91.2% and the best robustness against dense attack (or sparse attack) of 57.5% (or 54.8%).