Counterfeit liquid food products, including olive oil, honey and alcohol, are continuing to pose severe threats to the general public as counterfeiters adulterate the authentic content with cheaper and potentially harmful substitutes, and package them in authentic bottles. Existing solutions are often impractical for the general public as they require specialized and costly equipment as well as taking liquid samples. We overcome these limitations by proposing LiquidHash, a novel detection system that only requires the use of a commodity smartphone to detect adulterated liquid products without opening the bottles. LiquidHash leverages computer vision and machine learning techniques to extract characteristics of air bubbles formed by flipping a bottle. We implement LiquidHash and evaluate its feasibility with real-world experiments and achieve an overall detection accuracy of up to 95%.
|Title of host publication||MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|Publication status||Published - 2022 Jun 27|
|Event||20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022 - Portland, United States|
Duration: 2022 Jun 27 → 2022 Jul 1
|Name||MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services|
|Conference||20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022|
|Period||22/6/27 → 22/7/1|
Bibliographical noteFunding Information:
This research was partially supported by grants from the Singapore Ministry of Education Academic Research Fund Tier 1 (R-252-000-B48-114) and Yonsei University Research Fund (Grant No. 2021-22-0337).
© 2022 Owner/Author.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications