Our latest work was published in Nature Machine Intelligence this week: Woods, W., Chen, J. & Teuscher, C. Adversarial explanations for understanding image classification decisions and improved neural network robustness. Nat Mach Intell (2019) doi:10.1038/s42256-019-0104-6
“Deep neural networks can be led to misclassify an image when minute changes that are imperceptible to humans are introduced. While for some networks this ability can cast doubt on the reliability of the model, it also offers explainability for networks that use more robust regularization.”
Abstract: For sensitive problems, such as medical imaging or fraud detection, neural network (NN) adoption has been slow due to concerns about their reliability, leading to a number of algorithms for explaining their decisions. NNs have also been found to be vulnerable to a class of imperceptible attacks, called adversarial examples, which arbitrarily alter the output of the network. Here we demonstrate both that these attacks can invalidate previous attempts to explain the decisions of NNs, and that with very robust networks, the attacks themselves may be leveraged as explanations with greater fidelity to the model. We also show that the introduction of a novel regularization technique inspired by the Lipschitz constraint, alongside other proposed improvements including a half-Huber activation function, greatly improves the resistance of NNs to adversarial examples. On the ImageNet classification task, we demonstrate a network with an accuracy-robustness area (ARA) of 0.0053, an ARA 2.4 times greater than the previous state-of-the-art value. Improving the mechanisms by which NN decisions are understood is an important direction for both establishing trust in sensitive domains and learning more about the stimuli to which NNs respond.
Open Access pre-print: https://arxiv.org/abs/1906.02896
Podcast: https://www.stitcher.com/podcast/the-data-skeptic-podcast/e/67341825
Reddit thread: https://www.reddit.com/r/MachineLearning/comments/ds0st4/r_adversarial_explanations_for_understanding