26 citations · 47 across the 4 of their papers we have counts for
4 papers
ShadowNet: A Secure and Efficient On-device Model Inference System for Convolutional Neural Networks
Zhichuang Sun, Ruimin Sun, Changming Liu +3
With the increased usage of AI accelerators on mobile and edge devices, on-device machine learning (ML) is gaining popularity. Thousands of proprietary ML models are being deployed…
Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Apps
Zhichuang Sun, Ruimin Sun, Long Lu +1
On-device machine learning (ML) is quickly gaining popularity among mobile apps. It allows offline model inference while preserving user privacy. However, ML models, considered as…
PTrix: Efficient Hardware-Assisted Fuzzing for COTS Binary
Yaohui Chen, Dongliang Mu, Jun Xu +5
Despite its effectiveness in uncovering software defects, American Fuzzy Lop (AFL), one of the best grey-box fuzzers, is inefficient when fuzz-testing source-unavailable programs.…
OAT: Attesting Operation Integrity of Embedded Devices
Zhichuang Sun, Bo Feng, Long Lu +1
Due to the wide adoption of IoT/CPS systems, embedded devices(IoT frontends) become increasingly connected and mission-critical, which in turn has attracted advanced attacks (e.g.,…