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.

We are witnessing a surge in the reported cases of counterfeit liquid products in the market including olive oil, honey, and alcohol. Counterfeiters often adulterate the liquid products by replacing a large portion of the authentic content with cheaper substitutes (e.g., mixing vodka with cheaper alcohol or potentially toxic methanol). Exacerbating the problem, the counterfeits are packaged and sealed to factory standards, rendering it extremely difficult for an average consumer to identify them. While solutions exist, they are often impractical for the general public as they require specialized and costly equipment. To overcome these limitations, we propose LiquidHash, a novel counterfeit liquid food product detection system. LiquidHash is a practical solution that only requires the use of a commodity smartphone to detect adulterated liquid products without opening the bottles. LiquidHash works by detecting and tracking the shape and movement of air bubbles that form inside the bottles. We implement LiquidHash and evaluate its feasibility with real-world experiments under varying conditions with a total of more than 500 minutes of video recording and observe an overall detection accuracy of up to 95%.

Counterfeit liquid food products, including olive oil, honey and alcohol, are continuing to pose severe threats to the general public as counterfeiters adulterate the authentic content with cheaper and potentially harmful substitutes, and package them in authentic bottles. Existing solutions are often impractical for the general public as they require specialized and costly equipment as well as taking liquid samples. We overcome these limitations by proposing LiquidHash, a novel detection system that only requires the use of a commodity smartphone to detect adulterated liquid products without opening the bottles. LiquidHash leverages computer vision and machine learning techniques to extract characteristics of air bubbles formed by flipping a bottle. We implement LiquidHash and evaluate its feasibility with real-world experiments and achieve an overall detection accuracy of up to 95%.

Tiny hidden spy cameras concealed in sensitive locations including hotels and bathrooms are becoming a significant threat worldwide. These hidden cameras are easily purchasable and are extremely difficult to find with the naked eye due to their small form factor. The state-of-the-art solutions that aim to detect these cameras are limited as they require specialized equipment and yield low detection rates. Recent academic works propose to analyze the wireless traffic that hidden cameras generate. These proposals, however, are also limited because they assume wireless video streaming, while only being able to detect the presence of the hidden cameras, and not their locations. To overcome these limitations, we present LAPD, a novel hidden camera detection and localization system that leverages the time-of-flight (ToF) sensor on commodity smartphones. We implement LAPD as a smartphone app that emits laser signals from the ToF sensor, and use computer vision and machine learning techniques to locate the unique reflections from hidden cameras. We evaluate LAPD through comprehensive real-world experiments by recruiting 379 participants and observe that LAPD achieves an 88.9% hidden camera detection rate, while using just the naked eye yields only a 46.0% hidden camera detection rate.

Tiny spy cameras hidden in everyday objects are continuing to pose severe privacy threats to the general public as these cameras are often placed in sensitive locations such as hotels and restroom stalls. Commercially available "hidden camera detectors" have high false positive rates, and existing academic works detect (but cannot localize) only a subset of hidden cameras with wireless capabilities. We overcome these limitations by proposing LAPD, a novel hidden camera detection and localization system that leverages time-of-flight (ToF) sensors on commodity smartphones. 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, LAPD achieves significantly higher hidden camera detection rates compared to the naked eye and hidden camera detectors.

Eavesdropping on private conversations is one of the most common yet detrimental threats to privacy. A number of recent works have explored side-channels on smart devices for recording sounds without permission. This paper presents LidarPhone, a novel acoustic side-channel attack through the lidar sensors equipped in popular commodity robot vacuum cleaners. The core idea is to repurpose the lidar to a laser-based microphone that can sense sounds from subtle vibrations induced on nearby objects. LidarPhone carefully processes and extracts traces of sound signals from inherently noisy laser reflections to capture privacy sensitive information (such as speech emitted by a victim's computer speaker as the victim is engaged in a teleconferencing meeting; or known music clips from television shows emitted by a victim's TV set, potentially leaking the victim's political orientation or viewing preferences). We implement LidarPhone on a Xiaomi Roborock vacuum cleaning robot and evaluate the feasibility of the attack through comprehensive real-world experiments. We use the prototype to collect both spoken digits and music played by a computer speaker and a TV soundbar, of more than 30k utterances totaling over 19 hours of recorded audio. LidarPhone achieves approximately 91% and 90% average accuracies of digit and music classifications, respectively.

Private conversations are an attractive target for malicious actors intending to conduct audio eavesdropping attacks. Previous works discovered unexpected vectors for these attacks, such as analyzing high-speed video of objects adjacent to sound sources, or using WiFi signal information. We propose LidarPhone, a novel side-channel attack that exploits the lidar sensors in commodity robot vacuum cleaners to perform acoustic eavesdropping attacks. LidarPhone is able to detect the minute vibrations induced on objects that are near audio sources, and extract meaningful signals from inherently noisy raw lidar returns. We evaluate a realistic scenario for potential victims: recovering privacy-sensitive digits (e.g., credit card numbers, social security numbers) emitted by computer speakers during teleconferencing calls. We implement LidarPhone on a Xiaomi Roborock vacuum cleaning robot and perform a comprehensive series of real-world experiments to determine its performance. LidarPhone achieves up to 91% accuracy for digit classification.