Adversarial Image

Adversarial images are subtly altered images designed to deceive deep learning models, primarily image classifiers and more recently, vision-language models, into making incorrect predictions while appearing normal to humans. Current research focuses on developing more robust and transferable adversarial attacks, exploring various attack methods (e.g., gradient-based, generative, and frequency-domain manipulations) and defense mechanisms (e.g., adversarial training, purification, and anomaly detection). This field is crucial for understanding and mitigating the vulnerabilities of AI systems to malicious manipulation, impacting the security and reliability of applications ranging from autonomous driving to medical image analysis.