提高对抗鲁棒性的瓦瑟斯坦切片对抗训练

3区 计算机科学 Q1 Computer Science
Woojin Lee, Sungyoon Lee, Hoki Kim, Jaewook Lee
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引用次数: 0

摘要

最近,基于深度学习的模型在以前被认为极具挑战性的任务上取得了令人印象深刻的表现。然而,最近的研究表明,各种深度学习模型容易受到对抗数据样本的影响。在本文中,我们提出了切片瓦瑟斯坦对抗训练法,鼓励干净数据和对抗数据的对数分布彼此相似。我们使用 Wasserstein 度量来捕捉两个分布之间的不相似性,然后通过端到端的训练过程来对齐分布。我们通过提供对抗数据样本的泛化误差边界,介绍了我们研究动机的理论背景。我们在三个标准数据集上进行了实验,结果表明,与以前的方法相比,我们的方法对白盒攻击具有更强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Sliced Wasserstein adversarial training for improving adversarial robustness

Sliced Wasserstein adversarial training for improving adversarial robustness

Recently, deep-learning-based models have achieved impressive performance on tasks that were previously considered to be extremely challenging. However, recent works have shown that various deep learning models are susceptible to adversarial data samples. In this paper, we propose the sliced Wasserstein adversarial training method to encourage the logit distributions of clean and adversarial data to be similar to each other. We capture the dissimilarity between two distributions using the Wasserstein metric and then align distributions using an end-to-end training process. We present the theoretical background of the motivation for our study by providing generalization error bounds for adversarial data samples. We performed experiments on three standard datasets and the results demonstrate that our method is more robust against white box attacks compared to previous methods.

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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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