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Title: DeepShuffle: A Lightweight Defense Framework against Adversarial Fault Injection Attacks on Deep Neural Networks in Multi-Tenant Cloud-FPGA
FPGA virtualization has garnered significant industry and academic interests as it aims to enable multi-tenant cloud systems that can accommodate multiple users' circuits on a single FPGA. Although this approach greatly enhances the efficiency of hardware resource utilization, it also introduces new security concerns. As a representative study, one state-of-the-art (SOTA) adversarial fault injection attack, named Deep-Dup, exemplifies the vulnerabilities of off-chip data communication within the multi-tenant cloud-FPGA system. Deep-Dup attacks successfully demonstrate the complete failure of a wide range of Deep Neural Networks (DNNs) in a black-box setup, by only injecting fault to extremely small amounts of sensitive weight data transmissions, which are identified through a powerful differential evolution searching algorithm. Such emerging adversarial fault injection attack reveals the urgency of effective defense methodology to protect DNN applications on the multi-tenant cloud-FPGA system. This paper, for the first time, presents a novel moving-target-defense (MTD) oriented defense framework DeepShuffle, which could effectively protect DNNs on multi-tenant cloud-FPGA against the SOTA Deep-Dup attack, through a novel lightweight model parameter shuffling methodology. DeepShuffle effectively counters the Deep-Dup attack by altering the weight transmission sequence, which effectively prevents adversaries from identifying security-critical model parameters from the repeatability of weight transmission during each inference round. Importantly, DeepShuffle represents a training-free DNN defense methodology, which makes constructive use of the typologies of DNN architectures to achieve being lightweight. Moreover, the deployment of DeepShuffle neither requires any hardware modification nor suffers from any performance degradation. We evaluate DeepShuffle on the SOTA open-source FPGA-DNN accelerator, Vertical Tensor Accelerator (VTA), which represents the practice of real-world FPGA-DNN system developers. We then evaluate the performance overhead of DeepShuffle and find it only consumes an additional ~3% of the inference time compared to the unprotected baseline. DeepShuffle improves the robustness of various SOTA DNN architectures like VGG, ResNet, etc. against Deep-Dup by orders. It effectively reduces the efficacy of evolution searching-based adversarial fault injection attack close to random fault injection attack, e.g., on VGG-11, even after increasing the attacker's effort by 2.3x, our defense shows a ~93% improvement in accuracy, compared to the unprotected baseline.  more » « less
Award ID(s):
2019548 2411207 2153525 2342618
NSF-PAR ID:
10504115
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE
Date Published:
Journal Name:
2024 IEEE Symposium on Security and Privacy (SP)
ISSN:
2375-1207
Format(s):
Medium: X
Location:
San Francisco, CA, USA
Sponsoring Org:
National Science Foundation
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