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- LAPD55
LAPD is a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones that emits laser signals from the ToF sensor, and uses computer vision and machine learning techniques to locate the unique reflections from hidden cameras.
- Detecting counterfeit liquid food products in a sealed bottle using a smartphone camera18
LiquidHash is a practical solution that only requires the use of a commodity smartphone to detect adulterated liquid products without opening the bottles, and works by detecting and tracking the shape and movement of air bubbles that form inside the bottles.
- PowDew: Detecting Counterfeit Powdered Food Products using a Commodity Smartphone15
PowDew operates by capturing and analyzing the interaction of a water droplet with the powdered formula, focusing on the droplet motion, namely its spreading and penetration, and infers the formula's authenticity.
- EyeClouds12
A viable solution that makes use of existing techniques such as heat maps and gaze stripes, as well as attention clouds which are inspired by the general concept of word clouds are presented, which can be easily extended to many other purposes with various types of data.
- Exploring eye movement data with image-based clustering9
An enhancement of the EyeCloud approach that is based on standard word cloud layouts adapted to image thumbnails by exploiting image information to cluster and group the thumbnails that are visually attended is presented.
- CAMPrints: Leveraging the "Fingerprints" of Digital Cameras to Combat Image Theft4
CAMPrints verifies whether edited images found online contain camera fingerprints matching those of user-provided reference images, and significantly outperforms the state-of-the-art methods by up to 1.8 times.
- On Utilizing Smartphone Time-of-Flight Sensors to Detect Hidden Spy Cameras4
LAPD is a smartphone app that detects hidden cameras in real-time by transmitting laser signals from the ToF sensor and searching for unique signatures representing reflections from hidden camera lenses using computer vision and machine learning techniques.
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- On utilizing smartphone cameras to detect counterfeit liquid food products2
This work proposes LiquidHash, a novel detection system that only requires the use of a commodity smartphone to detect adulterated liquid products without opening the bottles, and leverages computer vision and machine learning techniques to extract characteristics of air bubbles formed by flipping a bottle.
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- [Surgical planning and mid-term effectiveness of four major lower extremity arthroplasties in patients with rheumatoid arthritis].1
When RA patients receive 4JA, adequate preoperative evaluation, rational selection of the timing and sequence of surgery, and maximal restoration of lower limb alignment can achieve good mid-term effectiveness.
- Poster: Discovering Unanticipated Semantic Leakage from Intermediate Representations–
An open-world semantic inference attack that reveals unanticipated leakage from intermediate representations in distributed AI systems without predefining target semantic attributes is presented.
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- What Do Neighbors Know? Open-World Semantic Inference Attack on Intermediate Representations–
This work presents REVEAL, an open-world semantic inference attack that discovers what an embedding leaks without prior assumptions, and demonstrates that open-world excess leakage is a consistent vulnerability.
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- Trustworthy Provenance for Physical and Digital Artifacts with Commodity Mobile Devices–
A physics-guided sensing methodology that treats cameras and mobile sensors as instruments for measuring latent physical and device-level signals enables practical provenance verification for physical products and digital content.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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