Evolving Robust Neural Architectures to Defend from Adversarial Attacks
This work addresses the critical issue of adversarial robustness in neural networks for security-sensitive applications, offering a new approach that is incremental but opens up new possibilities for exploration.
The paper tackles the problem of neural networks being vulnerable to adversarial attacks by proposing a novel neural architecture search method that evolves robust architectures, achieving robustness comparable to state-of-the-art adversarial training while using only non-adversarial samples.
Neural networks are prone to misclassify slightly modified input images. Recently, many defences have been proposed, but none have improved the robustness of neural networks consistently. Here, we propose to use adversarial attacks as a function evaluation to search for neural architectures that can resist such attacks automatically. Experiments on neural architecture search algorithms from the literature show that although accurate, they are not able to find robust architectures. A significant reason for this lies in their limited search space. By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of concatenation layers in the search, we were able to evolve an architecture that is inherently accurate on adversarial samples. Interestingly, this inherent robustness of the evolved architecture rivals state-of-the-art defences such as adversarial training while being trained only on the non-adversarial samples. Moreover, the evolved architecture makes use of some peculiar traits which might be useful for developing even more robust ones. Thus, the results here confirm that more robust architectures exist as well as opens up a new realm of feasibilities for the development and exploration of neural networks. Code available at http://bit.ly/RobustArchitectureSearch.