Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior

• URL: http://arxiv.org/abs/2502.21048v2
• Date: Mon, 31 Mar 2025 10:43:02 GMT
• Title: Data-free Universal Adversarial Perturbation with Pseudo-semantic Prior
• Authors: Chanhui Lee, Yeonghwan Song, Jeany Son,
• Abstract summary: Data-free Universal Adversarial Perturbation (UAP) is an image-agnostic adversarial attack that deceives deep neural networks using a single perturbation generated solely from random noise without relying on data priors.<n>We propose a novel data-free universal attack method that extracts pseudo-semantic priors directly from the UAPs during training to enrich the semantic content within the data-free UAP framework.<n>Our approach effectively leverages latent semantic information within UAPs via region sampling, enabling successful input transformations-typically ineffective in traditional data-free UAP methods due to the lack of semantic cues-
• Score: 4.3012765978447565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-free Universal Adversarial Perturbation (UAP) is an image-agnostic adversarial attack that deceives deep neural networks using a single perturbation generated solely from random noise without relying on data priors. However, traditional data-free UAP methods often suffer from limited transferability due to the absence of semantic content in random noise. To address this issue, we propose a novel data-free universal attack method that recursively extracts pseudo-semantic priors directly from the UAPs during training to enrich the semantic content within the data-free UAP framework. Our approach effectively leverages latent semantic information within UAPs via region sampling, enabling successful input transformations-typically ineffective in traditional data-free UAP methods due to the lack of semantic cues-and significantly enhancing black-box transferability. Furthermore, we introduce a sample reweighting technique to mitigate potential imbalances from random sampling and transformations, emphasizing hard examples less affected by the UAPs. Comprehensive experiments on ImageNet show that our method achieves state-of-the-art performance in average fooling rate by a substantial margin, notably improves attack transferability across various CNN architectures compared to existing data-free UAP methods, and even surpasses data-dependent UAP methods. Code is available at: https://github.com/ChnanChan/PSP-UAP.

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