ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks

no code implementations • 11 Nov 2020 • Zhichuang Sun, Ruimin Sun, Changming Liu, Amrita Roy Chowdhury, Long Lu, Somesh Jha

ShadowNet protects the model privacy with Trusted Execution Environment (TEE) while securely outsourcing the heavy linear layers of the model to the untrusted hardware accelerators.

Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps

1 code implementation • 18 Feb 2020 • Zhichuang Sun, Ruimin Sun, Long Lu, Alan Mislove

We found that, alarmingly, 41% of ML apps do not protect their models at all, which can be trivially stolen from app packages.

Face Recognition Malware Detection

PTrix: Efficient Hardware-Assisted Fuzzing for COTS Binary

1 code implementation • 25 May 2019 • Yao-Hui Chen, Dongliang Mu, Jun Xu, Zhichuang Sun, Wenbo Shen, Xinyu Xing, Long Lu, Bing Mao

This poor performance is caused by the slow extraction of code coverage information from highly compressed PT traces.

Software Engineering Cryptography and Security