Invention Grant
- Patent Title: Using gradients to detect backdoors in neural networks
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Application No.: US15953956Application Date: 2018-04-16
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Publication No.: US11132444B2Publication Date: 2021-09-28
- Inventor: Wilka Carvalho , Bryant Chen , Benjamin J. Edwards , Taesung Lee , Ian M. Molloy , Jialong Zhang
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Stephen J. Walder, Jr.; Jeffrey S. LaBaw
- Main IPC: G06F21/57
- IPC: G06F21/57 ; G06N3/08 ; G06N20/00

Abstract:
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.
Public/Granted literature
- US20190318099A1 Using Gradients to Detect Backdoors in Neural Networks Public/Granted day:2019-10-17
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