Vulnerabilities of Connectionist AI Applications: Evaluation and Defence

• Main Authors: Berghoff, Christian, Neu, Matthias, von Twickel, Arndt
• Format: Journal Article
• Language: English
• Published: 18-03-2020
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Cryptography and Security; Computer Science - Learning; Computer Science - Software Engineering; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2003.08837

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 as well as their AI-specific root
causes are discussed in detail. Additionally and in contrast to former reviews,
the whole AI supply chain is analysed 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 supply chains 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 defence 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.