4 papers
Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework
Yan Zhong, Gefei Chen, Qiufang Ma +4
Image enhancement and restoration have become standard back-end operations on short-video and social media platforms to boost UGC visual experience. Yet these processes inevitably…
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives
Chenxi Yang, Yujia Liu, Dingquan Li +2
Deep neural networks have demonstrated impressive success in No-Reference Image Quality Assessment (NR-IQA). However, recent researches highlight the vulnerability of NR-IQA models…
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization
Yujia Liu, Chenxi Yang, Dingquan Li +2
The task of No-Reference Image Quality Assessment (NR-IQA) is to estimate the quality score of an input image without additional information. NR-IQA models play a crucial role in t…
Exploring Vulnerabilities of No-Reference Image Quality Assessment Models: A Query-Based Black-Box Method
Chenxi Yang, Yujia Liu, Dingquan Li +1
No-Reference Image Quality Assessment (NR-IQA) aims to predict image quality scores consistent with human perception without relying on pristine reference images, serving as a cruc…