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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.25792 |
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| _version_ | 1866918151486177280 |
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| author | Branch, Alexander Pooladzandi, Omead Khosraviani, Radin Bhat, Sunay Gajanan Jiang, Jeffrey Pottie, Gregory |
| author_facet | Branch, Alexander Pooladzandi, Omead Khosraviani, Radin Bhat, Sunay Gajanan Jiang, Jeffrey Pottie, Gregory |
| contents | We introduce PureVQ-GAN, a defense against data poisoning that forces backdoor triggers through a discrete bottleneck using Vector-Quantized VAE with GAN discriminator. By quantizing poisoned images through a learned codebook, PureVQ-GAN destroys fine-grained trigger patterns while preserving semantic content. A GAN discriminator ensures outputs match the natural image distribution, preventing reconstruction of out-of-distribution perturbations. On CIFAR-10, PureVQ-GAN achieves 0% poison success rate (PSR) against Gradient Matching and Bullseye Polytope attacks, and 1.64% against Narcissus while maintaining 91-95% clean accuracy. Unlike diffusion-based defenses requiring hundreds of iterative refinement steps, PureVQ-GAN is over 50x faster, making it practical for real training pipelines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25792 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PUREVQ-GAN: Defending Data Poisoning Attacks through Vector-Quantized Bottlenecks Branch, Alexander Pooladzandi, Omead Khosraviani, Radin Bhat, Sunay Gajanan Jiang, Jeffrey Pottie, Gregory Artificial Intelligence Computer Vision and Pattern Recognition We introduce PureVQ-GAN, a defense against data poisoning that forces backdoor triggers through a discrete bottleneck using Vector-Quantized VAE with GAN discriminator. By quantizing poisoned images through a learned codebook, PureVQ-GAN destroys fine-grained trigger patterns while preserving semantic content. A GAN discriminator ensures outputs match the natural image distribution, preventing reconstruction of out-of-distribution perturbations. On CIFAR-10, PureVQ-GAN achieves 0% poison success rate (PSR) against Gradient Matching and Bullseye Polytope attacks, and 1.64% against Narcissus while maintaining 91-95% clean accuracy. Unlike diffusion-based defenses requiring hundreds of iterative refinement steps, PureVQ-GAN is over 50x faster, making it practical for real training pipelines. |
| title | PUREVQ-GAN: Defending Data Poisoning Attacks through Vector-Quantized Bottlenecks |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.25792 |