12 citations · 15 across the 7 of their papers we have counts for
7 papers
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…
ReuNify: A Step Towards Whole Program Analysis for React Native Android Apps
Yonghui Liu, Xiao Chen, Pei Liu +3
React Native is a widely-used open-source framework that facilitates the development of cross-platform mobile apps. The framework enables JavaScript code to interact with native-si…
ModelObfuscator: Obfuscating Model Information to Protect Deployed ML-based Systems
Mingyi Zhou, Xiang Gao, Jing Wu +4
More and more edge devices and mobile apps are leveraging deep learning (DL) capabilities. Deploying such models on devices -- referred to as on-device models -- rather than as rem…
Taming Android Fragmentation through Lightweight Crowdsourced Testing
Xiaoyu Sun, Xiao Chen, Yonghui Liu +2
Android fragmentation refers to the overwhelming diversity of Android devices and OS versions. These lead to the impossibility of testing an app on every supported device, leaving…
A Comparative Study of Smartphone and Smart TV Apps
Yonghui Liu, Xiao Chen, Yue Liu +6
Context: Smart TVs have become one of the most popular television types. Many app developers and service providers have designed TV versions for their smartphone applications. Desp…
Demystifying Hidden Sensitive Operations in Android apps
Xiaoyu Sun, Xiao Chen, Li Li +5
Security of Android devices is now paramount, given their wide adoption among consumers. As researchers develop tools for statically or dynamically detecting suspicious apps, malwa…
Mining Android API Usage to Generate Unit Test Cases for Pinpointing Compatibility Issues
Xiaoyu Sun, Xiao Chen, Yanjie Zhao +3
Despite being one of the largest and most popular projects, the official Android framework has only provided test cases for less than 30% of its APIs. Such a poor test case coverag…