Back to Search
Start Over
Detect Adversarial Attacks Against Deep Neural Networks With GPU Monitoring
- Source :
- IEEE Access, Vol 9, Pp 150579-150591 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- 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%.
- Subjects :
- General Computer Science
Computer science
Attack detection
Graphics processing unit
Machine learning
computer.software_genre
graphics processing unit
deep Neural Networks
Data modeling
Image (mathematics)
Adversarial system
Software
Robustness (computer science)
General Materials Science
business.industry
General Engineering
anomaly detection
TK1-9971
adversarial attacks
Deep neural networks
Artificial intelligence
Performance indicator
Electrical engineering. Electronics. Nuclear engineering
business
computer
image classification
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....e31bc11ecb1b0456b36e0e8c8f47e8b3