Awesome Adversarial Machine Learning
A curated list of resources for adversarial machine learning, covering attacks, defenses, and research.
What is Awesome Adversarial Machine Learning?
Awesome Adversarial Machine Learning is a curated collection of resources focused on the study of adversarial examples and attacks on machine learning models. It compiles key research papers, blog posts, and talks that explore how ML systems can be manipulated and how to defend against such threats. The list serves as a starting point for understanding vulnerabilities in neural networks and other models.
Machine learning researchers, AI security specialists, and practitioners interested in model robustness and adversarial attacks. It's particularly useful for those entering the field of adversarial ML or looking for foundational and state-of-the-art references.
It provides a centralized, vetted repository of essential adversarial ML resources, saving time for researchers and developers. Unlike generic ML lists, it focuses specifically on security and robustness, aggregating content from top experts in a structured, accessible format.
Overview
A curated list of awesome adversarial machine learning resources
Use Cases
Best For
- Finding seminal papers on adversarial attacks like FGSM or DeepFool
Related Projects
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