ChandlerBang

DANCE is a Python toolkit to support deep learning models for analyzing single-cell gene expression at scale. Our goal is to build up a deep learning community and benchmark platform for computational models in single-cell analysis.

DeepRobust is a pytorch library for adversarial attack and defense methods on images and graphs. It is demonstrated that some imperceptible perturbation on the input data can fool the systems based on deep learning.

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.