Adversarial Pattern

Adversarial patterns are carefully crafted inputs designed to deceive machine learning models, primarily focusing on image recognition, natural language processing, and autonomous systems. Current research investigates the creation and robustness of these patterns across various model architectures, including convolutional neural networks, transformers, and large language models, often employing optimization techniques to generate effective attacks. Understanding and mitigating the impact of adversarial patterns is crucial for ensuring the reliability and security of AI systems in diverse applications, ranging from autonomous driving to medical diagnosis and cybersecurity.