Computer Science > Computer Vision and Pattern Recognition
arXiv:2311.08265 (cs)
[Submitted on 14 Nov 2023]
Title:On The Relationship Between Universal Adversarial Attacks And Sparse Representations
View a PDF of the paper titled On The Relationship Between Universal Adversarial Attacks And Sparse Representations, by Dana Weitzner and Raja Giryes
View PDFAbstract:The prominent success of neural networks, mainly in computer vision tasks, is increasingly shadowed by their sensitivity to small, barely perceivable adversarial perturbations in image input.
In this work, we aim at explaining this vulnerability through the framework of sparsity.
We show the connection between adversarial attacks and sparse representations, with a focus on explaining the universality and transferability of adversarial examples in neural networks.
To this end, we show that sparse coding algorithms, and the neural network-based learned iterative shrinkage thresholding algorithm (LISTA) among them, suffer from this sensitivity, and that common attacks on neural networks can be expressed as attacks on the sparse representation of the input image. The phenomenon that we observe holds true also when the network is agnostic to the sparse representation and dictionary, and thus can provide a possible explanation for the universality and transferability of adversarial attacks.
The code is available atthis https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2311.08265 [cs.CV] |
| (orarXiv:2311.08265v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2311.08265 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1109/OJSP.2023.3244486 DOI(s) linking to related resources |
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View a PDF of the paper titled On The Relationship Between Universal Adversarial Attacks And Sparse Representations, by Dana Weitzner and Raja Giryes
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