- 专利标题: Using gradients to detect backdoors in neural networks
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申请号: US15953956申请日: 2018-04-16
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公开(公告)号: US11132444B2公开(公告)日: 2021-09-28
- 发明人: Wilka Carvalho , Bryant Chen , Benjamin J. Edwards , Taesung Lee , Ian M. Molloy , Jialong Zhang
- 申请人: International Business Machines Corporation
- 申请人地址: US NY Armonk
- 专利权人: International Business Machines Corporation
- 当前专利权人: International Business Machines Corporation
- 当前专利权人地址: US NY Armonk
- 代理商 Stephen J. Walder, Jr.; Jeffrey S. LaBaw
- 主分类号: G06F21/57
- IPC分类号: G06F21/57 ; G06N3/08 ; G06N20/00
摘要:
Mechanisms are provided for evaluating a trained machine learning model to determine whether the machine learning model has a backdoor trigger. The mechanisms process a test dataset to generate output classifications for the test dataset, and generate, for the test dataset, gradient data indicating a degree of change of elements within the test dataset based on the output generated by processing the test dataset. The mechanisms analyze the gradient data to identify a pattern of elements within the test dataset indicative of a backdoor trigger. The mechanisms generate, in response to the analysis identifying the pattern of elements indicative of a backdoor trigger, an output indicating the existence of the backdoor trigger in the trained machine learning model.
公开/授权文献
- US20190318099A1 Using Gradients to Detect Backdoors in Neural Networks 公开/授权日:2019-10-17
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