Abstract
In this paper, we introduce an imperceptible adversarial attack approach designed to effectively degrade the reconstruction quality of LIC, resulting in the reconstructed image being severely disrupted by noise where identifying any object in the reconstructed image is virtually impossible. More specifically, we generate adversarial examples by introducing a Frobenius norm-based loss function to maximize the discrepancy between original images and reconstructed images from adversarial examples in order to corrupt the reconstructed image severely.
| Original language | English |
|---|---|
| Title of host publication | 2024 Data Compression Conference, DCC 2024: Proceedings |
| Editors | Ali BILGIN, James E. FOWLER, Joan SERRA-SAGRISTA, Yan YE, James A. STORER |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 583 |
| Number of pages | 1 |
| ISBN (Electronic) | 9798350385878 |
| DOIs | |
| Publication status | Published - 2024 |
| Externally published | Yes |
| Event | 2024 Data Compression Conference - Snowbird, United States Duration: 19 Mar 2024 → 22 Mar 2024 |
Publication series
| Name | Data Compression Conference: Proceedings |
|---|---|
| Publisher | IEEE |
| ISSN (Print) | 1068-0314 |
| ISSN (Electronic) | 2375-0359 |
Conference
| Conference | 2024 Data Compression Conference |
|---|---|
| Abbreviated title | DCC 2024 |
| Country/Territory | United States |
| City | Snowbird |
| Period | 19/03/24 → 22/03/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- adversarial attack
- image compression
- invisible adversarial attack
- learned image compression
- robustness