They unveiled a technique to detect hidden cameras using the ToF sensor of smartphones

Researchers from the National University of Singapore and the University of Yonseo (Korea) have developed a method to detect hidden cameras in a room using an ordinary smartphone equipped with a ToF sensor.

This research stems from the current concern that today a hidden camera can be purchased for just over a dollar and these types of cameras have a lens with a diameter of 1-2 millimeters, which makes it difficult to find them indoors. In South Korea, more than 6.800 incidents involving the placement of hidden cameras in hotel rooms or bathrooms were recorded per year.

The method LAPD (Laser-Assisted Photography Detection) proposed by the researchers allows to detect hidden cameras using modern smartphones equipped with a depth sensor (ToF), which is used to estimate the distance to objects when focusing the camera and in augmented reality applications.

Among the equipment that were used, Samsung S20 and Huawei P30 Pro are mentioned as examples of smartphones that use these sensors. The sensor creates a depth map by scanning the surrounding area with a laser and calculating the distance based on the delayed arrival of the reflected beam.

Small spy cameras hidden in sensitive places like hotels and bathrooms that are becoming a major threat around the world. These hidden cameras can be easily purchased and are extremely difficult to find with the naked eye due to their small form factor. 
State-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and produce low detection.

The method of detecting hidden cameras relies on detecting abnormalities when lenses and lenses are illuminated with a laser, creating specific flares on the resulting depth map. Anomalies are detected by a learning algorithm automatic that can distinguish the specific glare of the camera. The study authors intend to publish a finished app for the Android platform after fixing some issues with API restrictions.

Recent academic papers propose to analyze wireless traffic generated by hidden cameras. These proposals, however, are also limited because they assume wireless video transmission, while only
can detect the presence of hidden cameras, and not their locations.

To overcome these limitations, we present LAPD, a innovative hidden camera detection and location system that takes advantage of the time-of-flight (ToF) sensor in basic smartphones.

The total time it takes to scan a room is estimated to be 30-60 seconds. In an experiment carried out with 379 volunteers, hidden cameras were detected with the LAPD method in 88,9% of the cases.

For comparison, only 46% of the participants in the experiment were able to find the cameras by eye and the efficiency of using the specialized K18 signal detector was 62,3% and 57,7%, depending on the scan mode. selected. The LAPD method also showed a lower false positive rate: 16.67% versus 26.9% / 35.2% for K18 and 54.9% when searching by eye.

We implement LAPD as a smartphone application that emits laser signals from the ToF sensor using computer vision and machine learning to locate unique reflections from hidden cameras.

We evaluate LAPD through comprehensive real-world experiments recruiting 379 participants and observe that LAPD achieves t88,9% hidden camera detection handle, while if used only with the naked eye produces only a 46,0% hidden camera detection rate.

LAPD detection accuracy is dependent on the hidden camera reaching a 20 degree angle of view of the sensor and is at the optimal distance from the sensor (too close, the camera lens flare is blurry, and if it is too far, it disappears).

To improve accuracy, it is proposed to use sensors with a higher resolution (on smartphones available to researchers, the ToF sensor resolution is 320 × 240, that is, the size of the anomaly in the image is only 1-2 pixels) and depth detail (now there are only 8 depth levels for each pixel).

Other methods of evaluating the presence of a hidden camera include wireless traffic analyzers that detect the presence of video transmission over a wireless network, as well as electromagnetic radiation scanners.

Finally, if you are interested in knowing more about it, you can consult the details In the following link.


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