The Feasibility and Inevitability of Stealth Attacks
Ivan Y. Tyukin, Desmond J. Higham, Alexander Bastounis, Eliyas, Woldegeorgis, Alexander N. Gorban

TL;DR
This paper demonstrates that AI systems, especially deep neural networks, are vulnerable to stealth attacks where minimal modifications can control outputs without detection, highlighting a significant security concern.
Contribution
It introduces new attack strategies that can subtly manipulate AI decisions while remaining undetectable, and analyzes their feasibility and implications.
Findings
Stealth attacks can be made transparent on fixed validation sets.
Single neuron modifications can cause significant vulnerabilities.
Strategies to defend against such attacks are proposed.
Abstract
We develop and study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence (AI) systems including deep learning neural networks. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself. Such a stealth attack could be conducted by a mischievous, corrupt or disgruntled member of a software development team. It could also be made by those wishing to exploit a ``democratization of AI'' agenda, where network architectures and trained parameter sets are shared publicly. We develop a range of new implementable attack strategies with accompanying analysis, showing that with high probability a stealth attack can be made transparent, in the sense that system performance is unchanged on a fixed validation set which is unknown to the attacker, while evoking any…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdversarial Robustness in Machine Learning
