DeepRobust- Pytorch Library for adversarial attack and defense in deep learning

I'm the lead author of the paper you cited. Glad you enjoyed our work :)

Sure, you can break systems. That doesn't mean that they aren't useful! In many cases a system will see the same boring input many times over. People are often willing to be a bit flexible and help out when it happens to misread something. The fact that you can intentionally break systems like that, and that you can break them in a particular direction, like making them always think there's no danger in an image, is really worrisome.

Our work shows that your autonomous car won't always work well; that its vision system has some systematic error which we can characterize now. Adversarial attacks show that someone can intentionally make your car see a lane, whenever and wherever they feel like it, and drive you off the road. It's a whole different ballgame, and the language of attack and defense really fits well.

Maybe we want the machines think as human think

Thank @0xab for answering this question. Actually I hold the same opinion as @0xab. Those "non-adversarial non-attack" happens sometimes and there also exist adversarial attacks where adversary intentionally craft some pictures to fool the system. In some cases, the system already achieves a pretty high performance and generalize very well on various datasets but they may fail on adversarial images. For example, attackers can put some carefully-crafted stickers on the STOP sign and the self-driving car will give wrong predictions. The existence of adversarial attacks poses great challenges in applying deep learning model in safety-critical scenarios.

Both non-adversarial non-attack and adversarial attack reflect the vulnerability of the current deep learning models. Thus it is of great significance to study these problem and find countermeasures against those attacks.