Adversarial Attacks and Defenses in AI Safety/Reliability

Dear Colleagues,

Deep learning (DL) is at the heart of the current rise of artificial intelligence. Meanwhile, machine learning (ML) models have made significant strides in various domains, revolutionizing industries and enabling groundbreaking applications. However, with the growing reliance on these models, concerns surrounding their vulnerability to adversarial attacks have also intensified, such as in images. Despite advances in computing power, pixel resolution, and frame rate, perturbations are often too small to be perceptible, yet they completely fool the deep learning models. On some occasions, data anonymization is necessary as it reduces the risk of unintended disclosure when sharing data between countries, industries, and even departments within the same company. It also reduces opportunities for identity theft to occur. Hence, advanced techniques in this area have attracted increasing attention from both machine learning and security communities and have become a hot research topic in recent years.

This Commemorative Special Issue welcomes the submission of papers based on original research about adversarial and federated machine learning. Historical reviews, as well as perspective analyses for the future in this field of research, will also be taken into consideration. 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;
8. Formal theory for adversarial leaning.

Dr. Hong Liu
Dr. Bowen Wang
Guest Editors

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