A Survey on Machine Learning Adversarial Attacks
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | A Survey on Machine Learning Adversarial Attacks |
| 2. | Creator | Author's name, affiliation, country | Flávio Luis de Mello; Federal University of Rio de Janeiro; Brazil |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | adversarial attack; machine learning; poisoning; privacy attack; trojoning; backdooring; evasion; reprogramming; countermeasures |
| 4. | Description | Abstract | It is becoming notorious several types of adversaries based on their threat model leverage vulnerabilities to compromise a machine learning system. Therefore, it is important to provide robustness to machine learning algorithms and systems against these adversaries. However, there are only a few strong countermeasures, which can be used in all types of attack scenarios to design a robust artificial intelligence system. This paper is structured and comprehensive overview of the research on attacks to machine learning systems and it tries to call the attention from developers and software houses to the security issues concerning machine learning. |
| 5. | Publisher | Organizing agency, location | Rede Nacional de Segurança da Informação e Criptografia |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2020-01-20 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://enigma.unb.br/index.php/enigma/article/view/76 |
| 10. | Identifier | Digital Object Identifier (DOI) | https://doi.org/10.17648/jisc.v7i1.76 |
| 11. | Source | Title; vol., no. (year) | Journal of Information Security and Cryptography (Enigma); Vol 7, No 1 (2020) |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2020 Journal of Information Security and Cryptography (Enigma) This work is licensed under a Creative Commons Attribution 4.0 International License. |