A Survey of Bit-Flip Attacks on Deep Neural Network and Corresponding Defense Methods
Cheng Qian, Ming Zhang, Yuanping Nie, Shuaibing Lu, Huayang Cao
Abstract
As the machine learning-related technology has made great progress in recent years, deep neural networks are widely used in many scenarios, including security-critical ones, which may incura great loss when DNN is compromised. 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. In addition, we point out several drawbacks to existing works, which can hopefully be researched in the future.
Topics & Concepts
Robustness (evolution)Computer scienceArtificial neural networkResilience (materials science)Deep neural networksDeep learningBit (key)Artificial intelligenceComputer securityMachine learningComputer engineeringThermodynamicsBiochemistryPhysicsGeneChemistryAdversarial Robustness in Machine LearningAdvanced Malware Detection TechniquesNetwork Security and Intrusion Detection