Evaluating Adversarial Robustness of No-Reference Image and Video Quality Assessment Models with Frequency-Masked Gradient Orthogonalization Adversarial Attack
- DOI
- 10.3390/bdcc9070166
- Published
- 2025-06-25
- Container
- Big Data and Cognitive Computing
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/bdcc9070166,
title = {Evaluating Adversarial Robustness of No-Reference Image and Video Quality Assessment Models with Frequency-Masked Gradient Orthogonalization Adversarial Attack},
author = {Khaled Abud and Sergey Lavrushkin and Dmitry Vatolin},
year = {2025},
journal = {Big Data and Cognitive Computing},
doi = {10.3390/bdcc9070166},
url = {https://doi.org/10.3390/bdcc9070166}
}RIS
TY - JOUR TI - Evaluating Adversarial Robustness of No-Reference Image and Video Quality Assessment Models with Frequency-Masked Gradient Orthogonalization Adversarial Attack AU - Khaled Abud AU - Sergey Lavrushkin AU - Dmitry Vatolin PY - 2025 JO - Big Data and Cognitive Computing DO - 10.3390/bdcc9070166 UR - https://doi.org/10.3390/bdcc9070166 ER -
APA
Abud, K., Lavrushkin, S., & Vatolin, D. (2025). Evaluating Adversarial Robustness of No-Reference Image and Video Quality Assessment Models with Frequency-Masked Gradient Orthogonalization Adversarial Attack. Big Data and Cognitive Computing. https://doi.org/10.3390/bdcc9070166
Source records
- crossref · retrieved 2026-11-03T13:30:47.802Z