Imperceptible Pattern

Imperceptible patterns research focuses on creating and detecting patterns that are undetectable by human perception or standard detection methods, yet significantly impact systems or models. Current research spans diverse applications, including camouflaged object detection (using hierarchical graph networks), adversarial attacks on various data types (exploiting feature interdependencies in tabular data and latent spaces in images), and watermarking techniques (leveraging receptive fields and key-centered schemes). This field is crucial for advancing security in machine learning, protecting intellectual property in digital media, and understanding the vulnerabilities of artificial intelligence systems to manipulation.