Image Attack

Image attacks involve the malicious manipulation of images to deceive machine learning models, primarily those used in computer vision tasks like object detection and image classification. Current research focuses on developing both more robust models and increasingly sophisticated attack methods, exploring techniques like adversarial perturbations (including universal perturbations) and prompt engineering to improve model resilience or enhance attack effectiveness. This field is crucial for ensuring the security and reliability of AI systems deployed in safety-critical applications, such as autonomous vehicles and content moderation, where adversarial examples could have significant real-world consequences.