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Sensitive Samples Revisited: Detecting Neural Network Attacks Using Constraint Solvers

Authors :
Docena, Amel Nestor
Wahl, Thomas
Pearce, Trevor
Fei, Yunsi
Source :
EPTCS 342, 2021, pp. 35-48
Publication Year :
2021

Abstract

Neural Networks are used today in numerous security- and safety-relevant domains and are, as such, a popular target of attacks that subvert their classification capabilities, by manipulating the network parameters. Prior work has introduced sensitive samples -- inputs highly sensitive to parameter changes -- to detect such manipulations, and proposed a gradient ascent-based approach to compute them. In this paper we offer an alternative, using symbolic constraint solvers. We model the network and a formal specification of a sensitive sample in the language of the solver and ask for a solution. This approach supports a rich class of queries, corresponding, for instance, to the presence of certain types of attacks. Unlike earlier techniques, our approach does not depend on convex search domains, or on the suitability of a starting point for the search. We address the performance limitations of constraint solvers by partitioning the search space for the solver, and exploring the partitions according to a balanced schedule that still retains completeness of the search. We demonstrate the impact of the use of solvers in terms of functionality and search efficiency, using a case study for the detection of Trojan attacks on Neural Networks.<br />Comment: In Proceedings SCSS 2021, arXiv:2109.02501

Details

Database :
arXiv
Journal :
EPTCS 342, 2021, pp. 35-48
Publication Type :
Report
Accession number :
edsarx.2109.03966
Document Type :
Working Paper
Full Text :
https://doi.org/10.4204/EPTCS.342.4