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To address this problem, we present a\nnew family of trust region based adversarial attacks, with the goal of\ncomputing adversarial perturbations efficiently. We propose several attacks\nbased on variants of the trust region optimization method. We test the proposed\nmethods on Cifar-10 and ImageNet datasets using several different models\nincluding AlexNet, ResNet-50, VGG-16, and DenseNet-121 models. Our methods\nachieve comparable results with the Carlini-Wagner (CW) attack, but with\nsignificant speed up of up to $37\\times$, for the VGG-16 model on a Titan Xp\nGPU. For the case of ResNet-50 on ImageNet, we can bring down its\nclassification accuracy to less than 0.1\\% with at most $1.5\\%$ relative\n$L_\\infty$ (or $L_2$) perturbation requiring only $1.02$ seconds as compared to\n$27.04$ seconds for the CW attack. We have open sourced our method which can be\naccessed at [1].","url_abs":"http://arxiv.org/abs/1812.06371v1","url_pdf":"http://arxiv.org/pdf/1812.06371v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"trust-region-based-adversarial-attack-on","repo_url":"https://github.com/amirgholami/trattack","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"trust-region-based-adversarial-attack-on","repo_url":"https://github.com/jeromerony/adversarial-library","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"adversarial-attack","task_name":"Adversarial Attack"},{"task_slug":null,"task_name":"GPU"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"local-response-normalization","method_name":"Local Response Normalization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.06371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.06371"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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