Thru-the-wall Eavesdropping on Loudspeakers via RFID by Capturing Sub-mm Level Vibration

2021 ◽  
Vol 5 (4) ◽  
pp. 1-25
Author(s):  
Chuyu Wang ◽  
Lei Xie ◽  
Yuancan Lin ◽  
Wei Wang ◽  
Yingying Chen ◽  
...  
Keyword(s):  
Frequency Band ◽  
Free Space ◽  
High Speed ◽  
Complex Frequency ◽  
Brick Wall ◽  
User Privacy ◽  
Generative Adversarial Network ◽  
Vibration Effect ◽  
Human Voice ◽  
Adversarial Network

The unprecedented success of speech recognition methods has stimulated the wide usage of intelligent audio systems, which provides new attack opportunities for stealing the user privacy through eavesdropping on the loudspeakers. Effective eavesdropping methods employ a high-speed camera, relying on LOS to measure object vibrations, or utilize WiFi MIMO antenna array, requiring to eavesdrop in quiet environments. In this paper, we explore the possibility of eavesdropping on the loudspeaker based on COTS RFID tags, which are prevalently deployed in many corners of our daily lives. We propose Tag-Bug that focuses on the human voice with complex frequency bands and performs the thru-the-wall eavesdropping on the loudspeaker by capturing sub-mm level vibration. Tag-Bug extracts sound characteristics through two means: (1) Vibration effect, where a tag directly vibrates caused by sounds; (2) Reflection effect, where a tag does not vibrate but senses the reflection signals from nearby vibrating objects. To amplify the influence of vibration signals, we design a new signal feature referred as Modulated Signal Difference (MSD) to reconstruct the sound from RF-signals. To improve the quality of the reconstructed sound for human voice recognition, we apply a Conditional Generative Adversarial Network (CGAN) to recover the full-frequency band from the partial-frequency band of the reconstructed sound. Extensive experiments on the USRP platform show that Tag-Bug can successfully capture the monotone sound when the loudness is larger than 60dB. Tag-Bug can efficiently recognize the numbers of human voice with 95.3%, 85.3% and 87.5% precision in the free-space eavesdropping, thru-the-brick-wall eavesdropping and thru-the-insulating-glass eavesdropping, respectively. Tag-Bug can also accurately recognize the letters with 87% precision in the free-space eavesdropping.

Generative Adversarial Network-based Channel Estimation in High-Speed Mobile Scenarios

2021 ◽  
Author(s):  
Danyang Zhang ◽  
Junhui Zhao ◽  
Lihua Yang ◽  
Yiwen Nie ◽  
Xiangcheng Lin
Keyword(s):  
Channel Estimation ◽  
High Speed ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals High-Speed Railway Intruding Object Image Generating with Generative Adversarial Networks

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3075 ◽  
Author(s):  
Baoqing Guo ◽  
Gan Geng ◽  
Liqiang Zhu ◽  
Hongmei Shi ◽  
Zujun Yu
Keyword(s):  
High Speed ◽  
Estimation Algorithm ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Foreign Object ◽  
Generative Adversarial Network ◽  
High Speed Railway ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Real Objects

Foreign object intrusion is a great threat to high-speed railway safety operations. Accurate foreign object intrusion detection is particularly important. As a result of the lack of intruding foreign object samples during the operational period, artificially generated ones will greatly benefit the development of the detection methods. In this paper, we propose a novel method to generate railway intruding object images based on an improved conditional deep convolutional generative adversarial network (C-DCGAN). It consists of a generator and multi-scale discriminators. Loss function is also improved so as to generate samples with a high quality and authenticity. The generator is extracted in order to generate foreign object images from input semantic labels. We synthesize the generated objects to the railway scene. To make the generated objects more similar to real objects, on scale in different positions of a railway scene, a scale estimation algorithm based on the gauge constant is proposed. The experimental results on the railway intruding object dataset show that the proposed C-DCGAN model outperforms several state-of-the-art methods and achieves a higher quality (the pixel-wise accuracy, mean intersection-over-union (mIoU), and mean average precision (mAP) are 80.46%, 0.65, and 0.69, respectively) and diversity (the Fréchet-Inception Distance (FID) score is 26.87) of generated samples. The mIoU of the real-generated pedestrian pairs reaches 0.85, and indicates a higher scale of accuracy for the generated intruding objects in the railway scene.

High-speed multimode fiber imaging system based on conditional generative adversarial network

2021 ◽  
Vol 19 (8) ◽  
pp. 081101
Author(s):  
Zhenming Yu ◽  
Zhenyu Ju ◽  
Xinlei Zhang ◽  
Ziyi Meng ◽  
Feifei Yin ◽  
...  
Keyword(s):  
High Speed ◽  
Imaging System ◽  
Multimode Fiber ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Reconstruction Method for Missing Measurement Data Based on Wasserstein Generative Adversarial Network

2021 ◽  
Vol 25 (2) ◽  
pp. 195-203
Author(s):  
Changfan Zhang ◽  
◽  
Hongrun Chen ◽  
Jing He ◽  
Haonan Yang
Keyword(s):  
High Speed ◽  
Measurement Data ◽  
Reconstruction Method ◽  
Reconstruction Accuracy ◽  
Distribution Law ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Context Similarity ◽  
High Speed Trains ◽  
Discrete Measurement

Focusing on the issue of missing measurement data caused by complex and changeable working conditions during the operation of high-speed trains, in this paper, a framework for the reconstruction of missing measurement data based on a generative adversarial network is proposed. Suitable parameters were set for each frame. Discrete measurement data are taken as the input of the frame for preprocessing the data dimensionality. The convolutional neural network then learns the correlation between different characteristic values of each device in an unsupervised pattern and constrains and improves the reconstruction accuracy by taking advantage of the context similarity of authenticity. It was determined experimentally that when there are different extents of missing measurement data, the model described in the present paper can still maintain a high reconstruction accuracy. In addition, the reconstruction data also conform well to the distribution law of the measurement data.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

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