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.
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.