Attacking machine learning with adversarial examples
•Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines.
•In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult.
هذا الخبر من OpenAI Blog. خبر يقدم أدوات ذكاء اصطناعي للتلخيص والترجمة والاستماع.
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult.المصدر: OpenAI Blog | Source: OpenAI Blog
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This article was originally published by OpenAI Blog. Khabr is a licensed Jordanian AI-powered news platform (Registration #82086). We add editorial value through: AI-powered news analysis, automated summaries, AI audio narration, multi-language translation (Arabic, English, French, Turkish), and AI fact-checking. Our mission is to make news more accessible and understandable for Arabic-speaking audiences worldwide.
