DAmageNet: A Universal Adversarial Dataset

Understand

It is now well known that deep neural networks (DNNs) are vulnerable to adversarial attack.

• Adversarial samples are similar to the clean ones, but are able to cheat the attacked DNN to produce incorrect predictions in high confidence.
• But most of the existing adversarial attacks have high success rate only when the information of the attacked DNN is well-known or could be estimated by massive queries.
• A promising way is to generate adversarial samples with high transferability.