2 citations · 4 across the 10 of their papers we have counts for
4 papers · 1 filter
DynaMO: Protecting Mobile DL Models through Coupling Obfuscated DL Operators
Mingyi Zhou, Xiang Gao, Xiao Chen +3
Deploying DL models on mobile Apps has become ever-more popular. However, existing studies show attackers can easily reverse-engineer mobile DL models in Apps to steal intellectual…
LLM for Mobile: An Initial Roadmap
Daihang Chen, Yonghui Liu, Mingyi Zhou +7
When mobile meets LLMs, mobile app users deserve to have more intelligent usage experiences. For this to happen, we argue that there is a strong need to appl LLMs for the mobile ec…
Model-less Is the Best Model: Generating Pure Code Implementations to Replace On-Device DL Models
Mingyi Zhou, Xiang Gao, Pei Liu +4
Recent studies show that deployed deep learning (DL) models such as those of Tensor Flow Lite (TFLite) can be easily extracted from real-world applications and devices by attackers…
Investigating White-Box Attacks for On-Device Models
Mingyi Zhou, Xiang Gao, Jing Wu +3
Numerous mobile apps have leveraged deep learning capabilities. However, on-device models are vulnerable to attacks as they can be easily extracted from their corresponding mobile…