Semantic Attack

Semantic attacks target the meaning and interpretation of data, aiming to manipulate machine learning models by subtly altering input features rather than directly modifying pixels or tokens. Current research focuses on developing and evaluating these attacks against various models, including large language models (LLMs), graph convolutional networks (GCNs), and multi-sensor fusion systems, often employing techniques like adversarial examples and data poisoning. Understanding and mitigating these attacks is crucial for ensuring the reliability and security of AI systems across diverse applications, from autonomous vehicles to natural language processing, as they can lead to misclassifications, biased outputs, and system failures.