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- W4387319128 abstract "This paper shows that Memristive Crossbar Array (MCA)-based neuromorphic architectures provide a robust defense against adversarial attacks due to the stochastic behavior of memristors. Furthermore, it shows that adversarial robustness can be further improved by compression-based preprocessing steps that can be implemented on MCAs. It also evaluates the effect of inter-chip process variations on adversarial robustness using the proposed MCA implementation and studies the effect of on-chip training. It shows that adversarial attacks do not uniformly affect the classification accuracy of different chips. Experimental evidence using a variety of datasets and attack models supports the impact of MCA-based neuromorphic architectures and compression-based preprocessing implemented using MCA on defending against adversarial attacks. It is also experimentally shown that the on-chip training results in high resiliency to adversarial attacks in all chips." @default.
- W4387319128 created "2023-10-04" @default.
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- W4387319128 date "2023-01-01" @default.
- W4387319128 modified "2023-10-05" @default.
- W4387319128 title "Memristive Crossbar Array-Based Adversarial Defense using Compression" @default.
- W4387319128 doi "https://doi.org/10.1109/tetc.2023.3319659" @default.
- W4387319128 hasPublicationYear "2023" @default.
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