Loading story…
Researchers at the Korea Advanced Institute of Science and Technology (KAIST), the National University of Singapore, and Singapore Management University developed SweepLED, a smartphone clip-on designed to locate concealed cameras in hotel rooms, short-term rentals, changing rooms, and other private spaces. The prototype uses a 15-LED grid to illuminate suspicious objects from changing angles while a smartphone captures the resulting reflection sequence.
SweepLED software compares LED-on and LED-off frames, follows lens-like reflections across viewpoints, and applies a spatio-temporal AI model to differentiate camera lenses from ordinary reflective surfaces. Testing across 30 objects, including 12 containing hidden cameras, produced reported accuracy of 93.9% in handheld use and 95.1% in static conditions; the core prototype components cost less than $7. The system is intended to detect physically concealed cameras and does not address publicly exposed internet-connected camera feeds or covert recording via smart glasses.
Track how attackers are adapting to this technology.
2 events from the most recent confirmed update back to the earliest known activity.
Women in South Korea protested over concerns about covert cameras, highlighting the privacy risks associated with hidden-camera surveillance.
Researchers from KAIST, the National University of Singapore, and Singapore Management University developed SweepLED, a smartphone-mounted LED accessory and AI system for identifying concealed camera lenses through changing reflection patterns. Testing on 30 objects, including 12 containing hidden cameras, produced 93.9% accuracy in handheld testing and 95.1% in static testing; the core hardware was estimated to cost less than $7.
Vulnerabilities, threat actors, malware, products, organizations, and breaches Mallory has linked to this story.
Follow how adversaries are adapting to this technology, and where it touches your stack today.
4 references tracked. Mallory keeps watching after this page renders.
cybersecuritynews.com
Open sourcehelpnetsecurity.com
Open sourcetomshardware.com
Open sourcechosun.com
Open sourceMap indicators from this story to your assets and identify affected systems in minutes.
Every observed campaign, victim, and pivot linked to actors named in this story.
Malware, exploits, and IOCs connected to the activity described here.
YARA, Sigma, and Snort rules deployed to your SIEM as soon as they’re published.
Get matching new stories delivered to your team as they break — not the next morning.
Ask questions about this story and take action on the answers.
Other stories Mallory is tracking that overlap on entities, sources, or topic labels.
An Android app called **Nearby Glasses** was released to alert users when certain camera-equipped smart glasses are nearby by scanning **Bluetooth Low Energy (BLE)** advertising traffic for **manufacturer company IDs** (Bluetooth SIG-assigned identifiers) rather than device names, MAC addresses, or service UUIDs, which can be inconsistent or randomized. The app, developed by **Yves Jeanrenaud** (Darmstadt University of Applied Sciences), runs as a foreground service and triggers notifications when detected devices meet a configurable RSSI threshold (reported default around `-75 dBm`, roughly 10–15 meters in open space). Reported monitored IDs include Meta/Luxottica and Snapchat-related identifiers, and users can add custom hex values to expand detection. Reporting highlighted that the approach can generate **false positives**, particularly from other Bluetooth devices made by the same vendors (e.g., other Meta hardware), and the project documentation warns against using detections to harass people. The app’s emergence is framed as a response to growing privacy concerns and reported misuse of smart glasses for **non-consensual recording** and demonstrations of **real-time facial recognition** using glasses paired with public data; coverage also notes legal and regulatory risk areas (e.g., state wiretapping laws) and ongoing debate over the effectiveness of recording indicator lights and the potential for those indicators to be disabled.
Jun 29, 2026
Hidden surveillance devices, such as covert cameras and microphones, are increasingly embedded in everyday objects like smoke detectors, clocks, and USB chargers. Detecting these devices requires careful observation of physical anomalies, such as misaligned fixtures or unusual reflections, and the use of tools like smartphone cameras to spot infrared emissions invisible to the naked eye. Awareness of subtle environmental cues and the physics of light and sound can help individuals identify hidden surveillance threats in both private and public spaces. Automated License Plate Readers (ALPRs) represent another form of pervasive surveillance, capturing and storing detailed information about vehicles and their movements across the United States. These AI-powered cameras, often used by law enforcement, can track a car's route and daily routine, raising significant privacy and data security concerns. Vulnerabilities in ALPR systems have led to data leaks, and the ease of access to these databases by police—sometimes without warrants—highlights the broader risks associated with the proliferation of surveillance technologies in modern society.
Jun 29, 2026
Researcher Bill Swearingen publicly unveiled **noRecognition**, a project that uses reinforcement learning to generate adversarial visual patterns designed to stop automated surveillance systems from recognizing people, objects, and vehicles while still allowing ordinary video recording. Swearingen said the system ran roughly **31 million tests** over the past year and produced increasingly effective patterns by learning from prior detection failures. In lab testing, Swearingen said the patterns defeated **11 open-source detection algorithms**, including software associated with **Flock** license plate readers, **Axon** body-worn camera workflows, and **Clearview AI**-enabled camera pipelines. He also demonstrated the concept at **Def Con** by covering a **2009 Toyota Yaris** with one of the patterns and testing it against a Flock camera, while keeping the most effective designs offline to slow vendor countermeasures and pursuing crowdfunding for derivative products such as shirts, hoodies, and possible vehicle wraps.
Aug 11, 2026