Галерея 3039295
Галерея 3039295
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Abstract: Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of source models transfer to other tar... View more
Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of source models transfer to other target models and, thus, pose a security threat to black-box applications (when attackers have no access to the target models). Current transfer-based ensemble attacks, however, only consider a limited number of source models to craft an adversarial example and, thus, obtain poor transferability. Besides, recent query-based black-box attacks, which require numerous queries to the target model, not only come under suspicion by the target model but also cause expensive query cost. In this article, we propose a novel transfer-based black-box attack, dubbed serial-minigroup-ensemble-attack (SMGEA). Concretely, SMGEA first divides a large number of pretrained white-box source models into several “minigroups.” For each minigroup, we design three new ensemble strategies to improve the intragroup transferability. Moreover, we propose a new algorithm that recursively accumulates the “long-term” gradient memories of the previous minigroup to the subsequent minigroup. This way, the learned adversarial information can be preserved, and the intergroup transferability can be improved. Experiments indicate that SMGEA not only achieves state-of-the-art black-box attack ability over several data sets but also deceives two online black-box saliency prediction systems in real world, i.e., DeepGaze-II (
https://deepgaze.bethgelab.org/
) and SALICON (
http://salicon.net/demo/
). Finally, we contribute a new code repository to promote research on adversarial attack and defense over ubiquitous pixel-to-pixel computer vision tasks. We share our code together with the pretrained substitute model zoo at
https://github.com/CZHQuality/AAA-Pix2pix
.
Date of Publication: 09 December 2020
References is not available for this document.
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Deep neural networks, despite their great success in computer vision, are susceptible to adversarial attacks [1]–[3]. The adversarial attacks add some quasi-imperceptible perturbations to the benign input to drastically change the model output. More importantly, some carefully designed adversarial examples can transfer across different models. That is, the adversarial inputs crafted against some pretrained source models can transfer to other unseen target models. Despite the source and target models adopting diverse architectures, they may share similar decision boundaries [4], [5]. Thus, if an adversarial example can fool multiple source models, it can, perhaps, capture the intrinsic transferable adversarial information that allows it to fool a broad class of target models. The transferability of adversarial examples provides an opportunity to launch black-box attacks when attackers have no access to the target model. On the contrary, white-box attacks require all information (e.g., architectures and parameters) regarding the target model; thus, they are not practical.
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Int J Biol Sci
v.7(2); 2011
PMC3039295
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Int J Biol Sci. 2011; 7(2): 147–153.
Published online 2011 Feb 4. doi: 10.7150/ijbs.7.147
1. Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
2. Department of Obstetrics and Gynecology, Changhua Christian Hospital, Changhua, Taiwan
3. Department of Surgery, Chung Shan Medical University, Taichung, Taiwan
4. Armed Forces Institute of Pathology and American Registry of Pathology, Washington DC, USA
✉ Corresponding author: Ming-Chih Chou, MD., PhD., Member, Board of Trustees, Professor, Department of Surgery, Chung Shan Medical University, Taichung, Taiwan. Tel: 886-4-24730022; Fax: 886-4-24723229; E-mail: wt.ude.umsc@etaudarg . Or Yan-gao Man, MD., PhD., Director of Gynecologic and Breast Research Laboratory, Department of Gynecologic and Breast Pathology, Armed Forces Institute of Pathology and American Registry of Pathology. Tel: 202-782-1612; Fax: 202-782-3939; E-mail: lim.dso.pifa@nam
Conflict of Interests: The authors have declared that no conflict of interest exists.
Received 2010 Nov 16; Accepted 2011 Feb 1.
Copyright © Ivyspring International Publisher. This is an open-access article distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by-nc-nd/3.0/). Reproduction is permitted for personal, noncommercial use, provided that the article is in whole, unmodified, and properly cited.
Keywords: Invasive lobular cancer, invasive ductal carcinoma, human breast cancers, myoepithelial cell layers
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