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Yunseo Park

dblp:118/7378 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0001-3077-7078ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Ubiquitous computing and smart environments · 100%
Network and information security
1 paper
Privacy and data protection · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Ubiquitous computing and smart environments
mobile sensing
1.012026
Hide-and-Sweep: Detecting Concealed Cameras via LED Illumination Sweeps · MobiSys 2026
Ubiquitous computing and smart environments › mobile sensing
smartphone sensing
1.012026
Hide-and-Sweep: Detecting Concealed Cameras via LED Illumination Sweeps · MobiSys 2026

Methods — techniques the papers use, named apart from their topics

reflection analysis · 2.0LED illumination sweep · 2.0
YearPublicationVenuePosition
2026 Hide-and-Sweep: Detecting Concealed Cameras via LED Illumination Sweeps
abstract
Hidden cameras have increasingly infiltrated hotel and Airbnb rooms, posing serious privacy risks. Detecting such cameras is challenging because they are visually inconspicuous and often embedded inside everyday objects. Even worse, existing handheld detectors are manual and also rely on single-angle illumination and hence suffer from high false-positive rates. We present SweepLED (pronounced "sweepled")1, a practical hidden camera detection system that operates on a commodity smartphone augmented with an unobtrusive LED-embedded case. SweepLED performs LED sweeping - a controlled sequence of multi-angle illumination - while the user simply holds the phone still by hand, enabling the camera to capture how reflections evolve under changing lighting. This reveals stable, lens-specific cues that distinguish hidden camera lenses from ordinary reflective objects, enabling robust detection with low user effort. We implement SweepLED using a compact hardware add-on and evaluate it in realistic environments containing 12 hidden-camera objects and 18 commonly reflective non-camera items. Our results demonstrate that SweepLED provides accurate and reliable hidden-camera detection using only unobtrusive smartphone-compatible hardware, achieving approximately 94% detection accuracy with a sweep time of under 5 s and a core component cost of less than USD $7.
Jonghyuk Yun, Jaeyoung Moon, Yunseo Park, Sean Rui Xiang Tan, Rajesh Krishna Balan, Jun Han 0001
MobiSys3