{"id":1165,"slug":"adversarial-attacks","name":"Adversarial attacks","short_description":"Adversarial attacks craft perturbed inputs to mislead machine learning models into producing incorrect outputs.","url":"https://github.com/topics/adversarial-attacks","github_count":1192,"created_by":null,"logo_url":null,"released":null,"wikipedia_url":"https://en.wikipedia.org/wiki/Adversarial_machine_learning","related_topics":[],"aliases":[],"github_url":null,"content":"\u003cp\u003eAdversarial attacks are techniques that craft intentionally perturbed inputs to mislead machine learning models into producing incorrect outputs. They are central to research in AI robustness, security, and trustworthiness.\u003c/p\u003e\n","created_at":"2026-03-11T00:01:07.522Z","updated_at":"2026-06-22T00:01:05.864Z","topic_url":"https://awesome.ecosyste.ms/api/v1/topics/adversarial-attacks","html_url":"https://awesome.ecosyste.ms/topics/adversarial-attacks","projects_url":"https://awesome.ecosyste.ms/api/v1/projects?keyword=adversarial-attacks","lists_url":"https://awesome.ecosyste.ms/api/v1/lists?topic=adversarial-attacks"}