With the development of the Android system and increasing screen size, the use of multi-window mode has become prevalent among users. However, the security and privacy implications associated with this mode have not been thoroughly investigated. This paper uncovers severe and unique security vulnerabilities in Android's multi-window mode, revealing several high-risk side-channels that facilitate diverse cross-window attacks, leading to significant breaches of user privacy. In detail, our research introduces WinSpy, a framework leveraging a newly discovered resource contention side-channel in multi-window mode to fingerprint app launches, web pages, and in-app activities, all without violating Android's permission framework. Our extensive evaluations demonstrate that WinSpy achieves high accuracy (from 70 to 80% detecting website and app launches to over 97% recognizing critical in-app activities). Additionally, we reveal that due to Android's lenient permission management for this mode, window apps can also use Inertial Measurement Unit sensors to launch attacks, such as inferring the user's touch positions outside the window with high precision. Furthermore, we propose systematic mitigations against these vulnerabilities.

This work explores how to physically watermark images generated by CMOS cameras using deliberately injected radio-frequency signals. CMOS camera imaging is ubiquitous in embedded systems such as smartphones, AR/VR headsets, drones, and other IoT platforms to capture photos and videos. In restricted environments, a property owner may wish to prevent unauthorized camera recordings depending on spatio-temporal context. Indelible watermarks can deter unauthorized recording. A key research challenge is how to find a reasonably general mechanism to surreptitiously inject watermarks without access to the camera. Existing methods typically rely on software-based watermarking or metadata generation, assuming cooperation from camera owners. However, adversaries can trivially disable metadata or watermarking functions to evade forensic analysis. To address this gap, our work explores an unconventional approach of watermarking non-cooperative cameras by injecting radio-frequency interference in the environment to affect the analog sensing process and inject defender-controlled patterns in the image output. Our analysis explains how the rolling shutter and Bayer filter hardware convert radio-frequency signals into color stripes with variable widths. Building upon model-based simulation, our prototype design encodes and extracts imperceptible watermarks with a bandwidth of up to 50 bits per image. Proof-of-concept evaluations in lab environments show that the proposed technique could support watermarking images with diverse background scenes and reveal future challenges of improving watermark bandwidth and injection distance.

With the increasing popularity of virtual reality applications, there is an increasing demand for more interactive entertainment, learning, social interactions, and other activities on eyewear devices. Recognizing users’ emotion and providing reliable feedback can significantly improve the immersive experience for users. However, previous works in emotion recognition required modifications to existing eyewear devices and the integration of additional sensors, or relied on specialized sensors in expensive commercial-grade eyewear devices, making direct deployment on existing consumer-grade eyewear devices challenging. In this paper, we proposeAccEmo, the first system that analyzes the data from the built-in accelerometer sensor on eyewear devices to accurately recognize human emotion.AccEmofirst employs signal processing technologies to process raw accelerometer data, and then uses a binary classification network to determine whether the accelerometer data is influenced by emotional changes. Subsequently,AccEmoproposes a network architecture based on residual neural network and channel-wise attention mechanism as a universal feature extractor to extract complex features related to human emotions from the accelerometer data. Finally,AccEmouses personalized classifiers to achieve emotion recognition for different users. Extensive performance evaluation ofAccEmoacross diverse users demonstrates an exceptional average accuracy of 94.3%. Additionally, the robustness ofAccEmois validated through evaluations in various scenarios, yielding promising results.

Geomagnetism-based indoor localization has great social and commercial value due to its pervasiveness and indepen-dence from extra infrastructure. To improve the distinguishability of geomagnetic signals as location clues, geomagnetic sequences are usually taken as input. Although longer input sequence can provide higher localization accuracy, it suffers from high response time in practice. To address the above, we first utilize short geomagnetic sequences as input, alleviating high response time, and propose an efficient single position estimation model, taking advantage of modified transformer to estimate position for each independent short sequence. Noticing the temporal dependency and the spatial consistency constraint during continuous positioning, we further propose a joint position estimation model to capture the correlations among consecutive short sequences, achieving higher accuracy with multiple short sequences. We have conducted extensive experiments in two typical trial sites, a narrow office area and a spacious parking lot. Experimental results show that the proposed approach outperforms state-of-the-art competing schemes, and the localization error is reduced by more than 32% with shorter geomagnetic sequences.

With the increasing popularity of voice-based applications, acoustic eavesdropping has become a serious threat to users’ privacy. While on smartphones the access to microphones needs an explicit user permission, acoustic eavesdropping attacks can rely on motion sensors (such as accelerometer and gyroscope), which access is unrestricted. However, previous instances of such attacks can only recognize a limited set of pre-trained words or phrases. In this paper, we present AccEar, an accelerometer-based acoustic eavesdropping attack that can reconstruct any audio played on the smartphone’s loudspeaker with unconstrained vocabulary. We show that an attacker can employ a conditional Generative Adversarial Network (cGAN) to reconstruct high-fidelity audio from low-frequency accelerometer signals. The presented cGAN model learns to recreate high-frequency components of the user’s voice from low-frequency accelerometer signals through spectrogram enhancement. We assess the feasibility and effectiveness of AccEar attack in a thorough set of experiments using audio from 16 public personalities. As shown by the results in both objective and subjective evaluations, AccEar successfully reconstructs user speeches from accelerometer signals in different scenarios including varying sampling rate, audio volume, device model, etc.