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Title: Reverse-engineering deep neural networks using floating-point timing side-channels
Trained Deep Neural Network (DNN) models have become valuable intellectual property. A new attack surface has emerged for DNNs: model reverse engineering. Several recent attempts have utilized various common side channels. However, recovering DNN parameters, weights and biases, remains a challenge. In this paper, we present a novel attack that utilizes a floating-point timing side channel to reverse-engineer parameters of multi-layer perceptron (MLP) models in software implementation, entirely and precisely. To the best of our knowledge, this is the first work that leverages a floating-point timing side-channel for effective DNN model recovery.  more » « less
Award ID(s):
1929300
PAR ID:
10192165
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Design Automation Conference
ISSN:
1055-1530
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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