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.

The prevalence of counterfeit infant formulas worldwide poses serious threats to infant health and safety, a concern highlighted by the notorious Melamine Milk Scandal that affected hundreds of thousands of children. The primary challenge in detecting counterfeit formulas lies in their sophisticated adulteration and substitution techniques. Such detection is feasible only in laboratory settings, making it nearly impossible for average consumers to test the formula before feeding their infants. To address this problem, we propose PowDew, a novel and practical system for detecting counterfeit infant formula that utilizes only a commodity smartphone. PowDew operates by capturing and analyzing the interaction of a water droplet with the powdered formula, focusing on the droplet motion, namely its spreading and penetration. Our insight is that the droplet motions are governed by powder-specific properties such as wettability and porosity. PowDew analyzes the subtle differences in droplet motions, and infers the formula's authenticity. To demonstrate PowDew's effectiveness, we implement PowDew and conduct comprehensive real-world experiments under varying conditions with different brands of powdered infant formula and adulterants. Our experiments result in a total of 12,000 minutes of video recordings of the droplet motions on various infant formulas, including authentic and altered. Our experiments demonstrate that PowDew yields an overall detection accuracy of up to 96.1%.

The rise of counterfeit powdered food products, exemplified by notorious incidents such as the Melamine Milk Scandal, poses significant risks to consumers. The primary challenge in identifying these counterfeit products comes from their intricate adulteration and substitution techniques. Currently, such identification methods are only viable in laboratory settings, making average consumers nearly impossible to authenticate their products. To address this limitation, we propose PowDew, a novel system that employs a smartphone to detect counterfeit powdered food products. PowDew utilizes the powder's physical property, namely droplet motion, as a basis for verification. Through real-world experiments, PowDew demonstrate a practicality with achieving an overall detection accuracy of up to 96.1%.

Continuous monitoring of patients in psychiatric seclusion rooms is essential yet challenging, particularly with staff shortages that can delay responses to sudden changes in patient conditions. To this end, we propose PsiMo, a remote patient state monitoring system using mmWave Frequency Modulated Continuous Wave (FMCW) radar. Unlike existing vision-based or wearable systems, PsiMo captures patient movements without compromising privacy or risking potential self-harm. Our system continuously monitors patient's state of motion to alert medical staff in the event of abnormal conditions, such as agitation. Our preliminary evaluation shows PsiMo achieves 97.0% accuracy in patient state classification, demonstrating its potential for effective, non-contact monitoring.

In this demonstration, we introduce the vulnerability of indices in unpooling autoencoders. We show that this small factor can be maliciously exploited by performing man-in-the-middle attacks to eavesdrop on the victim's data, resulting in reconstruction and adversarial attacks. Such attacks especially make systems that integrate collaborative inference operations vulnerable. This demo presentation will empirically show the feasibility of index-based attacks by launching reconstruction and adversarial attacks on embedded/mobile computing platforms.