Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks

Understand

This paper considers the problem of helping humans exercise scalable oversight over deep neural networks (DNNs).

• Adversarial examples can be useful by helping to reveal weaknesses in DNNs, but they can be difficult to interpret or draw actionable conclusions from.
• Some previous works have proposed using human-interpretable adversarial attacks including copy/paste attacks in which one natural image pasted into another causes an unexpected misclassification.
• We build on these with two contributions.