Frame Attack

Frame attacks exploit the vulnerabilities of machine learning models, particularly deep neural networks, by manipulating input data across multiple frames (e.g., in videos) or during training (data poisoning). Current research focuses on developing robust defenses, employing techniques like attention mechanisms to identify and mitigate adversarial perturbations in real-time, and certified defense methods that provide mathematically guaranteed robustness against these attacks, often utilizing novel abstract domains for improved precision. This area is crucial for securing applications relying on AI in safety-critical domains, such as autonomous driving and medical diagnosis, where the reliability of model predictions under attack is paramount.