Adversarial Machine Learning

Adversarial Machine Learning is a subfield of artificial intelligence focused on developing models that can withstand attacks from malicious inputs. This discipline explores how adversarial examples—carefully crafted inputs that deceive machine learning algorithms—can undermine system performance. By studying these vulnerabilities, researchers aim to enhance the robustness, security, and reliability of AI systems across various applications, including computer vision, natural language processing, and cybersecurity. Understanding adversarial attacks is crucial for building resilient AI solutions that maintain accuracy and integrity in real-world environments.