Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks. (arXiv:2211.10024v1 [cs.LG])

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. …

attacks automated copy networks neural networks paste