Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148567
Title: A new lightweight in-situ adversarial sample detector for edge deep neural network
Authors: Wang, Si
Liu, Wenye
Chang, Chip-Hong
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2021
Source: Wang, S., Liu, W. & Chang, C. (2021). A new lightweight in-situ adversarial sample detector for edge deep neural network. IEEE Journal of Emerging and Selected Topics in Circuits and Systems, 11(2), 252-266. https://dx.doi.org/10.1109/JETCAS.2021.3076101
Project: CHFA-GC1-AW01
Journal: IEEE Journal of Emerging and Selected Topics in Circuits and Systems
Abstract: The flourishing of Internet of Things (IoT) has rekindled on-premise computing to allow data to be analyzed closer to the source. To support edge Artificial Intelligence (AI), hardware accelerators, open-source AI model compilers and commercially available toolkits have evolved to facilitate the development and deployment of applications that use AI at its core. This paradigm shift in deep learning computations does not, however, reduce the vulnerability of deep neural networks (DNN) against adversarial attacks but introduces a difficult catch-up. This is because existing methodologies rely mainly on offline analysis to detect adversarial inputs, assuming that the deep learning model is implemented on a 32-bit floating-point graphical processing unit (GPU) instance. In this paper, we propose a new hardware-oriented approach for in-situ detection of adversarial inputs feeding through a spatial DNN accelerator architecture or a third-party DNN Intellectual Property (IP) implemented on the edge. Our method exploits controlled glitch injection into the clock signal of the DNN accelerator to maximize the information gain for the discrimination of adversarial and benign inputs. A light gradient boosting machine (lightGBM) is constructed by analyzing the prediction probability of unmutated and mutated models and the label change inconsistency between the adversarial and benign samples in the training dataset. With negligibly small hardware overhead, the glitch injection circuit and the trained lightGBM detector can be easily implemented alongside with the deep learning model on a Xilinx ZU9EG chip. The effectiveness of the proposed detector is validated against four state-of-the-art adversarial attacks on two different types and scales of DNN models, VGG16 and ResNet50, for a thousand-class visual object recognition application. The results show a significant increase in true positive rate and a substantial reduction in false positive rate on the Fast Gradient Sign Method (FGSM), Iterative-FGSM (I-FGSM), C&W and universal perturbation attacks compared with modern software-oriented adversarial sample detection methods.
URI: https://hdl.handle.net/10356/148567
ISSN: 2156-3357
DOI: 10.1109/JETCAS.2021.3076101
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JETCAS.2021.3076101.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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