ESEC/FSE 2020 (series) / Research Papers /
DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks
Thu 12 Nov 2020 08:00 - 08:02 at Virtual room 2 - ML Testing 2
Although deep neural networks have been very successful in image-classification tasks,
they are prone to adversarial attacks. To generate
adversarial inputs, there has emerged a wide variety of techniques,
such as black- and whitebox attacks for neural networks. In this paper,
we present \textsf{DeepSearch}\xspace, a novel fuzzing-based, query-efficient, blackbox attack for image
classifiers. Despite its
simplicity, \textsf{DeepSearch}{} is shown to be more effective in finding adversarial
inputs than state-of-the-art blackbox approaches. \textsf{DeepSearch}{} is
additionally able to generate the most subtle adversarial inputs in comparison to
these approaches.
Thu 12 Nov Times are displayed in time zone: (UTC) Coordinated Universal Time change
Thu 12 Nov
Times are displayed in time zone: (UTC) Coordinated Universal Time change
08:00 - 08:30 | ML Testing 2Journal First / Paper Presentations / Research Papers / Tool Demos / Visions and Reflections at Virtual room 2 | ||
08:00 2mTalk | DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks Research Papers DOI | ||
08:03 1mTalk | Machine Learning Based Test Data Generation for Safety-critical Software Paper Presentations Ján ČegiňFaculty of Informatics and Information Technologies Slovak Technical University | ||
08:05 1mTalk | Machine Learning Testing: Survey, Landscapes and Horizons Journal First Jie M. ZhangUniversity College London, UK, Mark HarmanUniversity College London, UK, Lei MaKyushu University, Yang LiuNanyang Technological University, Singapore | ||
08:07 1mTalk | Machine Translation Testing via Pathological Invariance Research Papers Shashij GuptaIIT Bombay, India, Pinjia HeETH Zurich, Switzerland, Clara MeisterETH Zurich, Switzerland, Zhendong SuETH Zurich DOI | ||
08:09 1mTalk | Model-Based Exploration of the Frontier of Behaviours for Deep Learning System Testing Research Papers DOI | ||
08:11 1mTalk | PRODeep: A Platform for Robustness Verification of Deep Neural Networks Tool Demos Renjue LiInstitute of Software at Chinese Academy of Sciences, China, Jianlin LiInstitute of Software at Chinese Academy of Sciences, China, Cheng-Chao HuangInstitute of Intelligent Software, China, Pengfei YangInstitute of Software at Chinese Academy of Sciences, China, Xiaowei HuangUniversity of Liverpool, Lijun ZhangInstitute of Software, Chinese Academy of Sciences, Bai XueInstitute of Software at Chinese Academy of Sciences, China, Holger HermannsSaarland University DOI | ||
08:13 1mTalk | Testing Machine Learning Code using Polyhedral Region Visions and Reflections Md Sohel AhmedNational Institute of Informatics, Japan, Fuyuki IshikawaNational Institute of Informatics, Mahito SugiyamaNational Institute of Informatics, Japan DOI | ||
08:15 15mTalk | Conversations on ML Testing 2 Paper Presentations Fuyuan ZhangMPI-SWS, Germany, Ján ČegiňFaculty of Informatics and Information Technologies Slovak Technical University, Mark HarmanUniversity College London, UK, Renjue LiInstitute of Software at Chinese Academy of Sciences, China, Shashij GuptaIIT Bombay, India, Vincenzo RiccioUSI Lugano, Switzerland, M: Shin YooKorea Advanced Institute of Science and Technology |