2019 Using Deep Neural Network Pdf Artificial Neural Network Deep Starting from introducing several commonly used bit flip methods, this paper concentrates on bit flips attacks aiming dnn and the corresponding defense methods. We analyze the threat models, methods design, and effect of attack and defense methods in detail, drawing some helpful conclusions about improving the robustness and resilience of dnn.
Bit Flips Attack Neural Network Weight Attack View a pdf of the paper titled bit flip attack: crushing neural network with progressive bit search, by adnan siraj rakin and zhezhi he and deliang fan. To conduct an efficient bit flip attack on weights, for the first time, we propose a bit flip attack (bfa) together with progressive bit search (pbs) technique, that can totally crush a fully functional quantized dnn and convert it to a random output generator with several bit flips. In this paper, we propose blind data adversarial bit flip attack (bdfa), a novel technique to enable bfa against dnn hardware without any access to the training or testing data. We summarize the mainstream bit flip attack methods; analyze the mathematical principles of the attacks; analyze and compare the effects and overheads of different attacks.
Pdf Distributed Deep Neural Network Based Middleware For Cyber In this paper, we propose blind data adversarial bit flip attack (bdfa), a novel technique to enable bfa against dnn hardware without any access to the training or testing data. We summarize the mainstream bit flip attack methods; analyze the mathematical principles of the attacks; analyze and compare the effects and overheads of different attacks. To counter bit flip attacks, corresponding defense methods have emerged. the work on analyzing and evaluating the security properties of the dnn is currently divided into two directions: theoretical analysis based and experiment based. Recognizing the documented susceptibility of real valued neural networks to such attacks and the comparative robustness of quantized neural networks (qnns), in this work, we introduce bfaverifier, the first verification framework designed to formally verify the absence of bit flip attacks against qnns or to identify all vulnerable parameters in. One of the first attack that aims at drastically dropping the performance of a model, by targeting its parameters (weights) stored in memory, is the bit flip attack (bfa). in this work, we point out several evaluation challenges related to the bfa. Abstract—recent research has demonstrated the severity and prevalence of bit flip attacks (bfas; e.g., with rowhammer tech niques) on deep neural networks (dnns).
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