Adversarial Machine Learning
Adversarial machine learning highlights the vulnerabilities in AI systems, particularly in security-critical environments. By understanding how attacks can fool machine learning models, researchers can develop more secure AI systems, essential for applications like facial recognition and autonomous driving.
Adversarial machine learning is a subfield of AI that focuses on how malicious actors can manipulate machine learning models by providing misleading input, often referred to as "adversarial attacks." For example, an AI model trained to recognize images might be tricked into misclassifying an object by slightly altering the image data. This area of research aims to improve the robustness and security of AI systems.