{"ID":2847110,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.01079","arxiv_id":"2511.01079","title":"T-MLA: A targeted multiscale log-exponential attack framework for neural image compression","abstract":"Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of classifiers. Existing adversarial attacks on NICs are often naive adaptations of pixel-space methods, overlooking the unique, structured nature of the compression pipeline. In this work, we propose a more advanced class of vulnerabilities by introducing T-MLA, the first targeted multiscale log-exponential attack framework. We introduce adversarial perturbations in the wavelet domain that concentrate on less perceptually salient coefficients, improving the stealth of the attack. Extensive evaluation across multiple state-of-the-art NIC architectures on standard image compression benchmarks reveals a large drop in reconstruction quality while the perturbations remain visually imperceptible. On standard NIC benchmarks, T-MLA achieves targeted degradation of reconstruction quality while improving perturbation imperceptibility (higher PSNR/VIF of the perturbed inputs) compared to PGD-style baselines at comparable attack success, as summarized in our main results. Our findings reveal a critical security flaw at the core of generative and content delivery pipelines.","short_abstract":"Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of classifiers. Existing adversarial attacks on NICs are often naive adaptations of pixel-space methods, overlooking the unique, structured nat...","url_abs":"https://arxiv.org/abs/2511.01079","url_pdf":"https://arxiv.org/pdf/2511.01079v2","authors":"[\"Nikolay I. Kalmykov\",\"Razan Dibo\",\"Kaiyu Shen\",\"Xu Zhonghan\",\"Anh-Huy Phan\",\"Yipeng Liu\",\"Ivan Oseledets\"]","published":"2025-11-02T21:06:33Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"math.NA\"]","methods":"[]","has_code":false}