Analysis of an Existing Method for Detecting Adversarial Attacks on Deep Neural Networks
- Title
- Analysis of an Existing Method for Detecting Adversarial Attacks on Deep Neural Networks
- Creator
- Lapina M.; Dudun G.; Kotlyarov D.; Rjevskaya N.; Subramanian S.J.
- Description
- Analyzes the existing method of detecting adversarial attacks on deep neural networks, proposed by researchers from Carnegie Mellon University and the Korean Institute of Advanced Technologies (KAIST) Ko, G. and Lim, G in 2021. Examines adversarial attacks, as well as the history of research on the topic. The paper considers the concepts of interpreted and not interpreted neural networks and features of methods of protection of the types of neural networks considered. The method for protecting against adversarial attacks is also considered to be applicable to both types of neural networks. An example of an attack simulation is given, which makes it possible to identify a sign showing that an attack has been committed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-1044 LNNS, pp. 316-329.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- adversarial attack; artificial intelligence; attack algorithm; information security; machine learning; malicious machine learning; neural network; pattern recognition
- Coverage
- Lapina M., North Caucasus Federal University, Stavropol, Russian Federation; Dudun G., North Caucasus Federal University, Stavropol, Russian Federation; Kotlyarov D., North Caucasus Federal University, Stavropol, Russian Federation; Rjevskaya N., North Caucasus Federal University, Stavropol, Russian Federation; Subramanian S.J., CHRIST University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303164009-4
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Lapina M.; Dudun G.; Kotlyarov D.; Rjevskaya N.; Subramanian S.J., “Analysis of an Existing Method for Detecting Adversarial Attacks on Deep Neural Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19230.