Keystroke snooping is an effective way to steal sensitive information from the victims. Recent research on acoustic emanation-based techniques has greatly improved the accessibility by non-professional adversaries. However, these approaches either require multiple smartphones or require specific placement of the smartphone relative to the keyboards, which tremendously restricts the application scenarios. In this article, we propose UltraSnoop, a training-free, transferable, and placement-agnostic scheme that manages to infer user’s input using a single smartphone placed within the range covered by a microphone and speaker. The innovation of Ultrasnoop is that we propose an ultrasonic anchor-keystroke positioning method and a Mel Frequency Cepstrum Coefficients clustering algorithm, synthesis of which could infer the relative position between the smartphone and the keyboard. Along with the keystroke time difference of arrival, our method could infer the keystrokes and even gradually improve the accuracy as the snooping proceeds. Our real-world experiments show that UltraSnoop could achieve more than 85% top-3 snooping accuracy when the smartphone is placed within the range of 30–60 cm from the keyboard.

With an increasing number of mobile devices equipped with screens and cameras, screen-camera communication (SCC) systems enable data exchange between devices conveniently and efficiently. By encoding data with spatial and temporal diversity on a screen, multiple users with a camera can receive data without setting up a wireless network. However, as the transmitter pushes the limits of increasing throughput with a high display rate, the receiver actually suffers from a low goodput caused by composite frames. Those frames cannot be decoded correctly with existing methods. To address this problem, we propose a reliable data recovery scheme named RescQR. In RescQR, a mixture separation scheme coupled with a dedicated frame border is proposed to separate composite frames. A Viterbi-based data recovery scheme is proposed to recover data from blurred regions in composite frames. Additionally, an auto-configuration method with the help of a front camera is proposed to adjust parameters automatically according to the estimated distance between the screen and the camera. Our prototype and experiments demonstrate that RescQR achieves a data goodput of 400+kbps even with standard QR codes, which significantly outperforms previous solutions.

Remote sensing imagery (RSI) object detection is critical to many applications, yet mainstream detectors analyse only spatial features and, because of spectral bias, fail to learn high-frequency information adequately, resulting in performance bottlenecks under cluttered backgrounds, distractors, and multi-scale targets, especially small ones. To break these limitations, we propose MSRS-DETR, an end-to-end framework that deeply fuses spatial and frequency cues. The approach introduces three key innovations: (1) C2f_FATNET, a frequency-attention-enhanced lightweight residual backbone that provides richer dual-domain features with fewer parameters; (2) an Entanglement Transformer Block (ETB) in the encoder that refines deep semantics via cross-domain frequency–spatial interaction and suppresses background interference; and (3) S2-CCFF, a shallow-feature-extended bidirectional fusion path that markedly improves the retention and utilisation of fine details for small objects. Experiments on HRSC2016 and ShipRSImageNet demonstrate the effectiveness and generalisation of this spatial–frequency paradigm: relative to the baseline, MSRS-DETR reduces parameters by $29.1\%$, boosts inference speed by $12.4\%$ and $8.4\%$, and raises mAP_50-95 by $1.69\%$ and $2.16\%$, respectively.

This paper presents ChirpMu, a system that encodes information into chirp symbols and embeds the symbols with music. Users enjoy music without realizing the existence of chirp sounds, while their smartphones can decode the information. ChirpMu can be used to broadcast information such as Wi-Fi secrets or coupons in shopping malls. It features novel chirp symbol design that can combat sound attenuation and environment noise. In addition, ChirpMu properly adjusts the portion of chirp symbols with music, so that the chirp symbols cannot be heard by users but can be decoded by smartphones with low error rate. Various real-world experiments show that ChirpMu can achieve low bit error rate.

Image-based indoor localization using smartphones has become popular, leveraging visual landmarks and fingerprint extraction for localization. Fingerprint density significantly affects accuracy, but collecting dense, high-resolution fingerprints during on-site surveys is labor-intensive and incurs high computation/storage costs during matching. Additionally, efficient fingerprint extraction often constrains users to specific shooting poses, with deviations markedly reducing localization accuracy. To address these challenges, we introduce ARGILS, an Automated Real-time Generative Image Localization System. The key idea is to use cross sparse sampling instead of dense sampling, generate fingerprint features for missing locations, and quickly match locations through feature orthogonal decomposition. Cross sparse sampling ensures full coverage of scene features and helps to generate missing fingerprints. To maintain high localization resolution with sparse sampling, we designed a distance-constrained generative adversarial network to generate fingerprints for unsampled locations. Additionally, we developed an orthogonal fingerprint extraction method to decompose image features into horizontal and vertical directions in 2D space. To improve robustness against obstacles, we implemented a scanning localization scheme using key frame filtering and clustering. We have implemented ARGILS and performed extensive real-world evaluations. Experiment results show that when reducing 75% site survey effort, the average location error of ARGILS is around 2.5m in a shopping mall, 48% higher than state-of-the-art methods. ARGILS can also efficiently speed up localization process, with the time consumption ranging from 0.1 to 0.3 seconds on smartphones of various configurations.

With the availability of mobile devices display and camera, Screen-Camera links for Visible Lite Communications (VLC) has attracted much more attention due to its convenience, infrastructure-free and security. In conventional Screen-camera link, the senders encode data bits into a barcode stream and receivers capture the barcode scream and decode the barcodes. But conventional Screen-camera communication systems all suffer from both CMOS rolling shutter and inter frame mixing problem when display rate is close to camera capture rate and this leads to a high block transfer error rate. In this paper, we propose a system called FareQR by adding an outline border to the barcode stream to help the receiver detect mixed frames and de-obfuscation each mixed frames into perfect QRCodes. We formulate the mixed frame de-obfuscation problem as a hard-decision-decoding task, and propose a Viterbi algorithm to resolve each block in the mixed areas. We test the FareQR and result demonstrate that our work can recover the mixed area and reduce the block transfer error rate.