AAAI'22 Tutorial on "Adversarial Machine Learning for Good", presented by Pin-Yu Chen @ IBM Research

Tutorial material and slides: https://sites.google.com/view/advml4good Presenter's webpage: https://sites.google.com/site/pinyuchenpage ---- Adversarial machine learning (AdvML) is one of the most rapidly growing research fields in machine learning (ML) and artificial intelligence (AI). It studies adversarial robustness of state-of-the-art ML models such as neural networks, spanning from attacks that identify limitations of current ML systems, defenses that strengthen the model performance against various adversarial threats, to verification tools that quantify the level of robustness for different applications. Beyond the recent advances in AdvML, this tutorial aims to provide fresh aspects on “what’s next in AdvML”, i.e., adversarial machine learning for good. The phrase “for good” has two-fold meanings – novel innovations and sustainability. First, this tutorial will introduce emerging and novel applications that leverage the lessons from AdvML to benefit mainstream ML tasks, which differ from the original objective of evaluating and improving adversarial robustness. The examples include (i) generating contrastive explanations and counterfactual examples; (ii) model reprogramming for data-efficient transfer learning; (iii) model watermarking and fingerprinting for AI governance and ownership regulation; and (iv) data cloaking for enhanced privacy. Second, with the explosive number of submissions related to adversarial robustness growing every year, this tutorial aims to discuss the sustainability of this young research field towards continuous and organic growth, in terms of research norms and ethics, current trends, open challenges, and future directions. The target audience will be ML/AI researchers who are familiar with AdvML, as well as researchers who are interested in entering this field. The speaker will also share his thoughts on industrial practices.