Bibliographic Details
| Title: |
A Survey on Attacks and Their Countermeasures in Deep Learning: Applications in Deep Neural Networks, Federated, Transfer, and Deep Reinforcement Learning |
| Authors: |
Haider Ali, Dian Chen, Matthew Harrington, Nathaniel Salazar, Mohannad Al Ameedi, Ahmad Faraz Khan, Ali R. Butt, Jin-Hee Cho |
| Source: |
IEEE Access, Vol 11, Pp 120095-120130 (2023) |
| Publisher Information: |
IEEE, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Attacks, defenses, deep neural networks, federated learning, transfer learning, deep reinforcement learning, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
Deep Learning (DL) techniques are being used in various critical applications like self-driving cars. DL techniques such as Deep Neural Networks (DNN), Deep Reinforcement Learning (DRL), Federated Learning (FL), and Transfer Learning (TL) are prone to adversarial attacks, which can make the DL techniques perform poorly. Developing such attacks and their countermeasures is the prerequisite for making artificial intelligence techniques robust, secure, and deployable. Previous survey papers only focused on one or two techniques and are outdated. They do not discuss application domains, datasets, and testbeds in detail. There is also a need to discuss the commonalities and differences among DL techniques. In this paper, we comprehensively discussed the attacks and defenses in four popular DL models, including DNN, DRL, FL, and TL. We also highlighted the application domains, datasets, metrics, and testbeds in these fields. One of our key contributions is to discuss the commonalities and differences among these DL techniques. Insights, lessons, and future research directions are also highlighted in detail. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10288459/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3326410 |
| Access URL: |
https://doaj.org/article/7edc1c85f14a42209fd09d65cc0749a6 |
| Accession Number: |
edsdoj.7edc1c85f14a42209fd09d65cc0749a6 |
| Database: |
Directory of Open Access Journals |
