Abstract
Several deep models for blind image quality assessment (BIQA) have been proposed during the past few years, with promising results on standard image quality datasets. However, generalization of BIQA models beyond the standard content remains a challenge. In this paper, we study basic adversarial attack techniques to assess the robustness of representative deep BIQA models. Our results show that adversarial images created for a simple substitute BIQA model (i.e. white-box scenario) are transferable as such and able to deceive also several other more complex BIQA models (i.e. black-box scenario). We also investigated some basic defense mechanisms. Our results indicate that re-training BIQA models with a dataset augmented with adversarial images improves robustness of several models, but at the cost of decreased quality prediction accuracy on genuine images.
Original language | English |
---|---|
Title of host publication | QoEVMA '22 |
Subtitle of host publication | Proceedings of the 2nd Workshop on Quality of Experience in Visual Multimedia Applications |
Publisher | Association for Computing Machinery |
Number of pages | 9 |
ISBN (Electronic) | 978-1-4503-9499-4 |
DOIs | |
Publication status | Published - 14 Oct 2022 |
Event | 2nd Workshop on Quality of Experience in Visual Multimedia Applications (QoEVMA) at ACM Multimedia - Lisbon, Portugal Duration: 10 Oct 2022 → 14 Oct 2022 https://2022.acmmm.org/ |
Publication series
Name | QoEVMA 2022 - Proceedings of the 2nd Workshop on Quality of Experience in Visual Multimedia Applications |
---|
Conference
Conference | 2nd Workshop on Quality of Experience in Visual Multimedia Applications (QoEVMA) at ACM Multimedia |
---|---|
Country/Territory | Portugal |
City | Lisbon |
Period | 10/10/22 → 14/10/22 |
Internet address |
Bibliographical note
Funding Information:This work was supported in part by Natural Science Foundation of China under grant 61772348, Guangdong ”Pearl River Talent Recruitment Program” under Grant 2019ZT08X603, and Shenzhen Technology R&D Fund under Grant 202008121558110.
Keywords
- Image quality assessment
- Quality of Experience
- Adversarial attacks
- Adversarial defenses