Trustworthy-AI-Group/Adversarial_Examples_Papers

A list of recent papers about adversarial learning

What it solves

Keeping up with adversarial-example research became hard when the original paper list started crashing. This repository offers a stable, maintained collection of the latest papers so researchers can stay current.

How it works

The project aggregates adversarial-example papers, including a snapshot of the original list up to 2023-09-01, and appends new entries as they appear. It also links to a separate list focused specifically on transfer-based attacks.

Who it’s for

Researchers, students, and security practitioners working on adversarial machine learning who need a reliable, up-to-date bibliography without depending on a single unstable website.

Highlights

  • Includes all papers from the original list up to 2023-09-01
  • Continuously updated with new papers as they are published
  • Provides a dedicated list for transfer-based attack papers
  • Accepts contributions from the community via email

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