Presentation
TBNet: A Neural Architectural Defense Framework Facilitating DNN Model Protection in Trusted Execution Environments
DescriptionTrusted Execution Environments (TEEs) have become a promising solution to secure DNN models on edge devices. However, existing solutions either provide inadequate protection or introduce large performance overhead. This paper presents TBNet, a TEE-based defense framework that protects DNN model from a neural architectural perspective. TBNet generates a novel Two-Branch substitution model, to exploit (1) the computational resources in untrusted Rich Execution Environment (REE) for latency reduction and (2) the physically-isolated TEE for model protection. Experimental results on a Raspberry Pi across diverse DNN model architectures and datasets demonstrate that TBNet achieves efficient model protection at a low cost.
Event Type
Research Manuscript
TimeWednesday, June 263:30pm - 3:45pm PDT
Location3002, 3rd Floor
AI
Security
AI/ML Security/Privacy