Deep neural networks (DNNs) are powerful, but they can make mistakes that
pose significant risks. A model performing well on a test set does not imply
safety in deployment, so it is important to have additional tools to understand
its flaws. Adversarial examples can help reveal weaknesses, but they are often
difficult for a human to interpret or draw generalizable, actionable
conclusions from. Some previous works have addressed this by studying
human-interpretable attacks. We build on these with three contributions. First,
we introduce a method termed Search for Natural Adversarial Features Using
Embeddings (SNAFUE) which offers a fully-automated method for finding
"copy/paste" attacks in which one natural image can be pasted into another in
order to induce an unrelated misclassification. Second, we use this to red team
an ImageNet classifier and identify hundreds of easily-describable sets of
vulnerabilities. Third, we compare this approach with other interpretability
tools by attempting to rediscover trojans. Our results suggest that SNAFUE can
be useful for interpreting DNNs and generating adversarial data for them. Code
is available at this https URL

介绍了一种名为 SNAFUE 的自动化方法，可用于识别神经网络的弱点，以找到‘复制 / 粘贴’攻击，利用这种方法可以生成对抗性数据。