Learning Based Attack

Learning-based attacks exploit vulnerabilities in machine learning models to compromise their performance or extract sensitive information. Current research focuses on developing increasingly sophisticated attacks, including those that transfer across different models or datasets, and those targeting specific model architectures like federated learning systems, graph neural networks, and adaptive neural networks, often employing gradient-based methods or reinforcement learning. These attacks highlight critical security risks in AI systems across various domains (image, text, audio, video, etc.), underscoring the need for robust defenses and prompting ongoing investigation into more secure model training and deployment strategies.