Boffins find way to use a standard smartphone to find hidden spy cams

Smartphones now have lasers so we're gonna use them to find voyeurs


Recent model smartphones can be smarter still about finding hidden cameras in their vicinity, if they take advantage of time-of-flight (ToF) sensors.

ToF is a measurement technique that relies on reflected light to quickly determine the distance of objects. ToF sensors are used in LIDAR (light detection and ranging) systems and in other applications that utilize SLAM (simultaneous localization and mapping) algorithms, all of which involve the analysis of the visible and near-visible spectrum.

These sensors have started showing up in smartphones recently – Apple's iPhone 12 and 13, and Samsung's Galaxy S20+, among others, include a laser-based Sony ToF sensor – for augmented reality applications and adding depth information to 2D imagery.

Now, four researchers based in Singapore and South Korea have another application in mind: They see ToF sensors as a way to spot concealed cameras.

Sriram Sami, Bangjie Sun, and Sean Rui Xiang Tan, from National University of Singapore, and Jun Han from Yonsei University, describe how this might be done in a paper [PDF] titled "LAPD: Hidden Spy Camera Detection using Smartphone Time-of-Flight Sensors".

Their research was presented at the 19th ACM Conference on Embedded Networked Sensor Systems earlier this week. You can see the pitch below.

Youtube Video

LAPD in this context has nothing to do with the Los Angeles Police Department, a likely association at least for US readers reared on Hollywood police procedurals. Rather, it stands for Laser-Assisted Photography Detection – a technique for ferreting out tiny concealed lenses by checking for unusually intense reflections in the scanned area.

Surreptitious spying with hidden cameras has become a global concern, according to the boffins.

"Tiny hidden spy cameras placed in sensitive locations such as hotel rooms and lavatories are increasingly a threat to individual privacy globally," the research paper explains. "For example, in South Korea alone, there were over 6,800 such reported cases in a single year."

Salacious snooping has become a particular issue for users of services like AirBnB, where the platform operator doesn't control room providers or guarantee trustworthiness.

There are dedicated signal detection devices for finding hidden cameras and other electronics like the CC308+ and the K18, to say nothing of what can be done with open source Wi-Fi analysis software.

But the researchers contend these can be difficult to use correctly. What's more, smartphones are commonplace these days, so adding an app like LAPD is likely to be more convenient than carrying a dedicated bug or signal detector at all times. LAPD's goal is to be accessible, usable, and accurate, and to judge by the results reported in the paper, it hits those marks.

"From our comprehensive experiments, LAPD achieves an 88.9 per cent hidden camera detection rate, compared to just using the naked eye which yields only a 46.0 per cent hidden camera detection rate," the paper reads.

The dedicated K18 signal detector managed detection rates of 62.3 per cent and 57.7 per cent using its continuous and blinking methods respectively.

And boffins' LAPD method produced the lowest overall false positive rate (16.67 per cent), compared to the two K18 modes (26.9 per cent and 35.2 per cent) and to the naked eye (54.9 per cent). The technique's strong results follow from its use of a deep learning filter that's been trained to remove false positives.

Sriram Sami, one of the researchers, told The Register in an email that he feels this project is a way to address what he described as asymmetric warfare.

"The 'attackers' have all the power to place hidden cameras anywhere, and the public is, in contrast, generally defenseless," he explained. "That's why we're doing this work, and why we hope hidden camera detection can become more commonplace."

Sami said he intends to release the source code for LAPD but has to coordinate that with his colleagues. ®

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