An Empirical Study of AI Techniques in Mobile Applications
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Abstract (arXiv)
The integration of artificial intelligence (AI) into mobile applications has significantly transformed various domains, enhancing user experiences and providing personalized services through advanced machine learning (ML) and deep learning (DL) technologies. AI-driven mobile apps typically refer to applications that leverage ML/DL technologies to perform key tasks such as image recognition and natural language processing. In this paper, we conducted the most extensive empirical study on AI applications, exploring on-device ML apps, on-device DL apps, and AI service-supported (cloud-based) apps. Our study encompasses 56,682 real-world AI applications, focusing on three crucial perspectives: 1) Application analysis, where we analyze the popularity of AI apps and investigate the update states of AI apps; 2) Framework and model analysis, where we analyze AI framework usage and AI model protection; 3) User analysis, where we examine user privacy protection and user review attitudes. Our study has strong implications for AI app developers, users, and AI R&D. On one hand, our findings highlight the growing trend of AI integration in mobile applications, demonstrating the widespread adoption of various AI frameworks and models. On the other hand, our findings emphasize the need for robust model protection to enhance app security. Additionally, our study highlights the importance of user privacy and presents user attitudes towards the AI technologies utilized in current AI apps. We provide our AI app dataset (currently the most extensive AI app dataset) as an open-source resource for future research on AI technologies utilized in mobile applications.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 53 references that are in the map. First posted on arXiv 3 Dec 2022.
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18 of 53 references.
References · 18
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
- 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
- 2016Stealing Machine Learning Models via Prediction APIscited by 602
- 2018AMC: AutoML for Model Compression and Acceleration on Mobile Devicescited by 375
- 2017A Survey of Model Compression and Acceleration for Deep Neural Networkscited by 298
- 2020TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemscited by 161
- 2018Deep Learning in Mobile and Wireless Networking: A Surveycited by 117
- 2014Collaborative Deep Learning for Recommender Systemscited by 95
- 2018A First Look at Deep Learning Apps on Smartphonescited by 47
- 2021DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injectioncited by 42
6 more
- 2013Dropout improves Recurrent Neural Networks for Handwriting Recognitioncited by 39
- 2020Mind Your Weight(s): A Large-scale Study on Insufficient Machine Learning Model Protection in Mobile Appscited by 35
- 2021An Empirical Study on Deployment Faults of Deep Learning Based Mobile Applicationscited by 20
- 2021Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Appscited by 19
- 2022Understanding Real-world Threats to Deep Learning Models in Android Appscited by 15
- 2022Smart App Attack: Hacking Deep Learning Models in Android Appscited by 12
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