SEGA: A Transferable Signed Ensemble Gaussian Black-Box Attack Against No-Reference Image Quality Assessment Models

This research introduces SEGA, a method for attacking No-Reference Image Quality Assessment models (AI systems that evaluate image quality without comparing to a reference image) in black-box scenarios where attackers cannot see the target model's code. SEGA works by using Gaussian smoothing (a mathematical technique that approximates gradients, or the direction of change in the model) across multiple source models and applying a filter to make attacks harder to detect. The method successfully demonstrates improved ability to transfer attacks across different NR-IQA models.