2018/11/08 by Mengwei Xu, Xu, Mengwei, Jiawei Liu +9 · 6 citations
Computer Science · Engineering · #Advanced Malware Detection Techniques #Computers and Society (cs.CY) #FOS: Computer and information sciences #Green IT and Sustainability #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #cs.CY #cs.LG
paper · pdf · doi:10.48550/arxiv.1812.05448
openalex publication_date 2018/11/08 · arxiv created 2021/01/13 · arxiv updated 2021/01/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We are in the dawn of deep learning explosion for smartphones. To bridge the gap between research and practice, we present the first empirical study on 16,500 the most popular Android apps, demystifying how smartphone apps exploit deep learning in the wild. To this end, we build a new static tool that dissects apps and analyzes their deep learning functions. Our study answers threefold questions: what are the early adopter apps of deep learning, what do they use deep learning for, and how do their deep learning models look like. Our study has strong implications for app developers, smartphone vendors, and deep learning R&D. On one hand, our findings paint a promising picture of deep learning for smartphones, showing the prosperity of mobile deep learning frameworks as well as the prosperity of apps building their cores atop deep learning. On the other hand, our findings urge optimizations on deep learning models deployed on smartphones, the protection of these models, and validation of research ideas on these models.