CLC number: TP37
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-06-26
Cited: 0
Clicked: 1348
Citations: Bibtex RefMan EndNote GB/T7714
Wen LI, Hengyou WANG, Lianzhi HUO, Qiang HE, Linlin CHEN, Zhiquan HE, Wing W. Y. Ng. Low-rank matrix recovery with total generalized variation for defending adversarial examples[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(3): 432-445.
@article{title="Low-rank matrix recovery with total generalized variation for defending adversarial examples",
author="Wen LI, Hengyou WANG, Lianzhi HUO, Qiang HE, Linlin CHEN, Zhiquan HE, Wing W. Y. Ng",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="3",
pages="432-445",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300017"
}
%0 Journal Article
%T Low-rank matrix recovery with total generalized variation for defending adversarial examples
%A Wen LI
%A Hengyou WANG
%A Lianzhi HUO
%A Qiang HE
%A Linlin CHEN
%A Zhiquan HE
%A Wing W. Y. Ng
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 3
%P 432-445
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300017
TY - JOUR
T1 - Low-rank matrix recovery with total generalized variation for defending adversarial examples
A1 - Wen LI
A1 - Hengyou WANG
A1 - Lianzhi HUO
A1 - Qiang HE
A1 - Linlin CHEN
A1 - Zhiquan HE
A1 - Wing W. Y. Ng
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 3
SP - 432
EP - 445
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300017
Abstract: low-rank matrix decomposition with first-order total variation (TV) regularization exhibits excellent performance in exploration of image structure. Taking advantage of its excellent performance in image denoising, we apply it to improve the robustness of deep neural networks. However, although TV regularization can improve the robustness of the model, it reduces the accuracy of normal samples due to its over-smoothing. In our work, we develop a new low-rank matrix recovery model, called LRTGV, which incorporates total generalized variation (TGV) regularization into the reweighted low-rank matrix recovery model. In the proposed model, TGV is used to better reconstruct texture information without over-smoothing. The reweighted nuclear norm and L1-norm can enhance the global structure information. Thus, the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the image. To solve the challenging optimal model issue, we propose an algorithm based on the alternating direction method of multipliers. Experimental results show that the proposed algorithm has a certain defense capability against black-box attacks, and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.
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