Vulnerabilities of Connectionist AI Applications: Evaluation and Defense.
IT security
adversarial attack
artificial intelligence
certification
interpretability
neural network
poisoning attack
Journal
Frontiers in big data
ISSN: 2624-909X
Titre abrégé: Front Big Data
Pays: Switzerland
ID NLM: 101770603
Informations de publication
Date de publication:
2020
2020
Historique:
received:
20
03
2020
accepted:
10
06
2020
entrez:
11
3
2021
pubmed:
12
3
2021
medline:
12
3
2021
Statut:
epublish
Résumé
This article deals with the IT security of connectionist artificial intelligence (AI) applications, focusing on threats to integrity, one of the three IT security goals. Such threats are for instance most relevant in prominent AI computer vision applications. In order to present a holistic view on the IT security goal integrity, many additional aspects, such as interpretability, robustness and documentation are taken into account. A comprehensive list of threats and possible mitigations is presented by reviewing the state-of-the-art literature. AI-specific vulnerabilities, such as adversarial attacks and poisoning attacks are discussed in detail, together with key factors underlying them. Additionally and in contrast to former reviews, the whole AI life cycle is analyzed with respect to vulnerabilities, including the planning, data acquisition, training, evaluation and operation phases. The discussion of mitigations is likewise not restricted to the level of the AI system itself but rather advocates viewing AI systems in the context of their life cycles and their embeddings in larger IT infrastructures and hardware devices. Based on this and the observation that adaptive attackers may circumvent any single published AI-specific defense to date, the article concludes that single protective measures are not sufficient but rather multiple measures on different levels have to be combined to achieve a minimum level of IT security for AI applications.
Identifiants
pubmed: 33693396
doi: 10.3389/fdata.2020.00023
pmc: PMC7931957
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
23Informations de copyright
Copyright © 2020 Berghoff, Neu and von Twickel.
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