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- W4372340834 abstract "Backdoor-based watermarking schemes were proposed to protect the intellectual property of deep neural networks under the black-box setting. However, additional security risks emerge after the schemes have been published for as forensics tools. This paper reveals the capsulation attack that can easily invalidate most established backdoor-based watermarking schemes without sacrificing the pirated model’s functionality. By encapsulating the deep neural network with a filter, an adversary can block abnormal queries and reject the ownership verification. We propose a metric to measure a backdoor-based watermarking scheme’s security against the capsulation attack, and design a new backdoor-based deep neural network watermarking scheme that is secure against the capsulation attack by inverting the encoding process." @default.
- W4372340834 created "2023-05-07" @default.
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- W4372340834 date "2023-06-04" @default.
- W4372340834 modified "2023-10-05" @default.
- W4372340834 title "Measure and Countermeasure of the Capsulation Attack Against Backdoor-Based Deep Neural Network Watermarks" @default.
- W4372340834 cites W2115733720 @default.
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- W4372340834 doi "https://doi.org/10.1109/icassp49357.2023.10096448" @default.
- W4372340834 hasPublicationYear "2023" @default.
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