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
- Learning about defense strategies such as adversarial training or distillation
- Researching adversarial examples in computer vision and NLP
- Exploring talks and blog posts from leading adversarial ML researchers
- Getting started with adversarial machine learning concepts
- Studying real-world applications and case studies of ML security
Not Ideal For
- Researchers needing the most recent adversarial ML papers and trends post-2018
- Practitioners seeking ready-to-run code libraries or software tools for adversarial testing
- Teams building production systems that require up-to-date defense implementations and benchmarking suites
Pros & Cons
Pros
Aggregates seminal works like 'Explaining and Harnessing Adversarial Examples' by Goodfellow et al. and 'Intriguing properties of neural networks' by Szegedy et al., providing a solid starting point for understanding core concepts.
Includes key blogs from researchers like Andrej Karpathy and Nicolas Papernot, along with talks from conferences such as USENIX Enigma, offering insights directly from leading experts in the field.
Organizes resources by type (blogs, papers, talks) and subtopics such as attack methodologies and defense strategies, making it easy to navigate specific areas like reinforcement learning or speech recognition.
Part of the 'awesome' list ecosystem with a badge, indicating it has been vetted by the community for high-quality, relevant resources in adversarial machine learning.
Cons
The README explicitly states 'I no longer include up-to-date papers', so it misses recent advancements post-2018 and may contain broken links or outdated information.
Focuses solely on theoretical papers, blogs, and talks with no links to code repositories, libraries, or hands-on tutorials, limiting its utility for implementation-focused developers.
Most resources are from 2014-2018, lacking coverage of newer domains like adversarial robustness in large language models or recent defense techniques such as randomized smoothing or certifiable defenses.
Frequently Asked Questions
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