Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring

Deep Neural Networks (DNNs) are the preferred choice for image-based machine learning applications in several domains. However, DNNs are vulnerable to adversarial attacks, that are carefully-crafted perturbations introduced on input images to fool a DNN model. Adversarial attacks may prevent the app...

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Autores principales:	Tommaso Zoppi, Andrea Ceccarelli
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Attack detection anomaly detection graphics processing unit deep Neural Networks adversarial attacks image classification Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/212646a711e04129ba84416ccb6de4ac
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id	oai:doaj.org-article:212646a711e04129ba84416ccb6de4ac
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spelling	oai:doaj.org-article:212646a711e04129ba84416ccb6de4ac2021-11-18T00:08:24ZDetect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring2169-353610.1109/ACCESS.2021.3125920https://doaj.org/article/212646a711e04129ba84416ccb6de4ac2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605606/https://doaj.org/toc/2169-3536Deep Neural Networks (DNNs) are the preferred choice for image-based machine learning applications in several domains. However, DNNs are vulnerable to adversarial attacks, that are carefully-crafted perturbations introduced on input images to fool a DNN model. Adversarial attacks may prevent the application of DNNs in security-critical tasks: consequently, relevant research effort is put in securing DNNs. Typical approaches either increase model robustness, or add detection capabilities in the model, or operate on the input data. Instead, in this paper we propose to detect ongoing attacks through monitoring performance indicators of the underlying Graphics Processing Unit (GPU). In fact, adversarial attacks generate images that activate neurons of DNNs in a different way than legitimate images. This also causes an alteration of GPU activities, that can be observed through software monitors and anomaly detectors. This paper presents our monitoring and detection system, and an extensive experimental analysis that includes a total of 14 adversarial attacks, 3 datasets, and 12 models. Results show that, despite limitations on the monitoring resolution, adversarial attacks can be detected in most cases, with peaks of detection accuracy above 90%.Tommaso ZoppiAndrea CeccarelliIEEEarticleAttack detectionanomaly detectiongraphics processing unitdeep Neural Networksadversarial attacksimage classificationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150579-150591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Attack detection anomaly detection graphics processing unit deep Neural Networks adversarial attacks image classification Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Attack detection anomaly detection graphics processing unit deep Neural Networks adversarial attacks image classification Electrical engineering. Electronics. Nuclear engineering TK1-9971 Tommaso Zoppi Andrea Ceccarelli Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring
description	Deep Neural Networks (DNNs) are the preferred choice for image-based machine learning applications in several domains. However, DNNs are vulnerable to adversarial attacks, that are carefully-crafted perturbations introduced on input images to fool a DNN model. Adversarial attacks may prevent the application of DNNs in security-critical tasks: consequently, relevant research effort is put in securing DNNs. Typical approaches either increase model robustness, or add detection capabilities in the model, or operate on the input data. Instead, in this paper we propose to detect ongoing attacks through monitoring performance indicators of the underlying Graphics Processing Unit (GPU). In fact, adversarial attacks generate images that activate neurons of DNNs in a different way than legitimate images. This also causes an alteration of GPU activities, that can be observed through software monitors and anomaly detectors. This paper presents our monitoring and detection system, and an extensive experimental analysis that includes a total of 14 adversarial attacks, 3 datasets, and 12 models. Results show that, despite limitations on the monitoring resolution, adversarial attacks can be detected in most cases, with peaks of detection accuracy above 90%.
format	article
author	Tommaso Zoppi Andrea Ceccarelli
author_facet	Tommaso Zoppi Andrea Ceccarelli
author_sort	Tommaso Zoppi
title	Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring
title_short	Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring
title_full	Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring
title_fullStr	Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring
title_full_unstemmed	Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring
title_sort	detect adversarial attacks against deep neural networks with gpu monitoring
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/212646a711e04129ba84416ccb6de4ac
work_keys_str_mv	AT tommasozoppi detectadversarialattacksagainstdeepneuralnetworkswithgpumonitoring AT andreaceccarelli detectadversarialattacksagainstdeepneuralnetworkswithgpumonitoring
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Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring

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