Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability. We propose the fault sneaking a ack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking a ack with two constraints: 1) the classi cation of the other images should be unchanged and 2) the parameter modi cations should be minimized. Speci cally, the rst constraint requires us not only to inject designated faults (misclassi cations), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. e second constraint requires us to minimize the parameter modi cations (using 0 norm to measure the number of modi cations and 2 norm to measure the magnitude of modi cations). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance. CCS CONCEPTS•Security and privacy → Domain-speci c security and privacy architectures; Network security; •Networks → Network performance analysis; • eory of computation → eory and algorithms for application domains; KEYWORDSDeep neural networks, Fault injection, ADMM ACM Reference format:

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