EffTEE: Efficient Image Classification and Object Detection on Mobile Devices Using Trusted Execution Environments

Abstract

Deep neural networks (DNNs) play a crucial role in image classification and object detection, with applications in autonomous driving, security, Unmanned Aerial Vehicle (UAV) navigation, and robotics. Ensuring secure execution on mobile devices is challenging due to the sensitivity of input data and DNN architectures. Hardware-based Trusted Execution Environments (TEEs), such as ARM TrustZone, offer security but face resource and performance limitations when handling full-scale DNNs. This work introduces EffTEE, a security framework that enables efficient DNN execution within mobile TEEs. EffTEE employs dynamic suppression to prune unimportant neurons, foundational neuron restructuring to optimize memory usage, and dynamic slicing for effective model partitioning. Experimental results show that EffTEE reduces inference time by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 - 60 \times $ </tex-math></inline-formula> while maintaining accuracy comparable to existing secure DNN methods. These findings demonstrate EffTEE’s potential for secure and efficient DNN deployment in resource-constrained environments.