Adversarial Machine Learning: Attacks, Defenses and Security

Dear Colleagues,

Deep learning has become the gold standard in the area of current artificial intelligence; however, although deep models have exhibited remarkable success in artificial intelligence tasks, they are susceptible to small, imperceptible changes in test instances. This vulnerability poses a serious threat to the robustness of deep models, resulting in significant security issues. Consequently, adversarial machine learning has garnered increasing attention from the artificial intelligence, machine learning, computer vision, and security communities, making it a prominent topic of discussion in recent years.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

1. Foundations of understanding adversarial machine learning;
2. Theories and algorithms for adversarial attacking;
3. Robustness certification and property verification techniques;
4. Adversarial defense against different adversarial attacks;
5. Adversarial detection techniques against various adversarial attacks;
6. Empirical analysis of adversarial machine learning;
7. Novel applications of adversarial machine learning.

Dr. Hong Liu
Guest Editor

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• adversarial machine learning
• adversarial attacks and defense
• robustness certification
• deep learning
• applications