Adversarial Attacks

Adversarial attacks are deliberate manipulations of input data designed to deceive machine learning models, particularly in fields like computer vision and natural language processing. By introducing subtle perturbations, attackers can cause models to make incorrect predictions or classifications, exposing vulnerabilities in AI systems. These attacks highlight the need for robust security measures and defenses in artificial intelligence, ensuring the reliability and integrity of machine learning applications. Understanding adversarial attacks is crucial for researchers, developers, and organizations seeking to enhance AI resilience and safeguard against potential threats.