Eavesdropping via microphones has been a serious threat to security and privacy. Recent advances in utilizing non-linearity property of microphone amplifiers enable ultrasonic transducers to act as jammers. Due to the advantages of low-cost, inaudibility, and high jamming performance, those ultrasonic microphone jammers are promising in resisting covert eavesdropping. However, there are some barriers to their effective implementation. On one hand, existing approaches do not support authorized devices to record clean audios, which severely limits the usage of ultrasonic jammers. On the other hand, the unauthorized adversary can utilize noise reduction methods to recover the original audios, while current ultrasonic jammers cannot combat such attacks. In this paper, we propose a secure and selective microphone jamming system, which can prevent unauthorized devices from eavesdropping and ensure authorized recording devices operate normally. We utilize ultrasounds to jam unauthorized recording devices. Meanwhile, jamming noise is delivered through multiple wireless channels to authorized devices, which can use adaptive noise filter to remove the noise. Moreover, we specifically utilize multiple broadband jamming signals to improve the security of our microphone jamming system and defend against several adversary audio recovery methods. Experimental results show that less than 1% of words in unauthorized recordings can be recognized while in authorized recordings 92% of words can be recognized. Furthermore, even using noise reduction methods, 95.9% of words still cannot be recognized in unauthorized recordings.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Abuelma’atti, M.T.: Analysis of the effect of radio frequency interference on the dc performance of bipolar operational amplifiers. IEEE Trans. Electromagn. Compat. 45(2), 453–458 (2003)
Anand, S.A., Saxena, N.: Speechless: analyzing the threat to speech privacy from smartphone motion sensors. In: IEEE Symposium on Security and Privacy (2018)
Artem, R.: Google is permanently nerfing all home minis because mine spied on everything i said 24/7. https://www.androidpolice.com/2017/10/10/google-nerfing-home-minis-mine-spied-everything-said-247/#1. Accessed 7 July 2021
Ba, Z., Zheng, T., Zhang, X., Qin, Z., Li, B., Liu, X., Ren, K.: Learning-based practical smartphone eavesdropping with built-in accelerometer. In: Network and Distributed System Security Symposium (2020)
Becker, S., Ackermann, M., Lapuschkin, S., Müller, K.R., Samek, W.: Interpreting and explaining deep neural networks for classification of audio signals. arXiv preprint arXiv:1807.03418 (2018)
Chen, G.K.C., Whalen, J.J.: Comparative rfi performance of bipolar operational amplifiers. In: IEEE International Symposium on Electromagnetic Compatibility (1981)
Chen, Y., Li, H., Nagels, S., Li, Z., Lopes, P., Zhao, B.Y., Zheng, H.: Understanding the effectiveness of ultrasonic microphone jammer. CoRR abs/1904.08490 (2019)
Chen, Y., Li, H., Teng, S.Y., Nagels, S., Li, Z., Lopes, P., Zhao, B.Y., Zheng, H.: Wearable microphone jamming. In: ACM International Conference on Human Factors in Computing Systems (2020)
Gago, J., Balcells, J., GonzÁlez, D., Lamich, M., Mon, J., Santolaria, A.: Emi susceptibility model of signal conditioning circuits based on operational amplifiers. IEEE Trans. Electromagn. Compat. 49(4), 849–859 (2007)
Gao, M., Lin, F., Xu, W., Nuermaimaiti, M., Han, J., Xu, W., Ren, K.: Deaf-aid: mobile iot communication exploiting stealthy speaker-to-gyroscope channel. In: ACM International Conference on Mobile Computing and Networking (2020)
Google: Google Home Mini. https://store.google.com/us/product/google_home_mini_first_gen?hl=en-US. Accessed 7 July 2021
Hayes, M.H.: Statistical Digital Signal Processing and Modeling. Wiley, Hoboken (1996)
Haykin, S.: Adaptive filter theory (2002), ISBN-13: 978-0132671453
He, Y., Bian, J., Tong, X., Qian, Z., Zhu, W., Tian, X., Wang, X.: Canceling inaudible voice commands against voice control systems. In: ACM International Conference on Mobile Computing and Networking (2019)
Kune, D.F., Backes, J., Clark, S.S., Kramer, D., Reynolds, M., Fu, K., Kim, Y., Xu, W.: Ghost talk: Mitigating emi signal injection attacks against analog sensors. In: IEEE Symposium on Security and Privacy (2013)
Kwong, A., Xu, W., Fu, K.: Hard drive of hearing: Disks that eavesdrop with a synthesized microphone. In: IEEE Symposium on Security and Privacy (2019)
Li, L., Liu, M., Yao, Y., Dang, F., Cao, Z., Liu, Y.: Patronus: Preventing unauthorized speech recordings with support for selective unscrambling. In: ACM International Conference on Embedded Networked Sensor Systems (2020)
Lin, Q., An, Z., Yang, L.: Rebooting ultrasonic positioning systems for ultrasound-incapable smart devices. In: ACM International Conference on Mobile Computing and Networking (2019)
Michalevsky, Y., Boneh, D., Nakibly, G.: Gyrophone: Recognizing speech from gyroscope signals. In: USENIX Security Symposium (2014)
Panayotov, V., Chen, G., Povey, D., Khudanpur, S.: Librispeech: An asr corpus based on public domain audio books. In: IEEE International Conference on Acoustics, Speech and Signal Processing (2015)
Recommendation, I.T.: Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs. Rec. ITU-T P. 862 (2001)
Roy, N., Hassanieh, H., Roy Choudhury, R.: Backdoor: Making microphones hear inaudible sounds. In: ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services (2017)
Roy, N., Shen, S., Hassanieh, H., Choudhury, R.R.: Inaudible voice commands: The long-range attack and defense. In: 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18), pp. 547–560. USENIX Association (2018)
Schroeder, M.R., Atal, B.S., Hall, J.L.: Optimizing digital speech coders by exploiting masking properties of the human ear. J. Acoust. Soc. Am. 66(6), 1647–1652 (1979)
Shen, H., Zhang, W., Fang, H., Ma, Z., Yu, N.: Jamsys: coverage optimization of a microphone jamming system based on ultrasounds. IEEE Access 7, 67483–67496 (2019)
Sun, K., Chen, C., Zhang, X.: ”alexa, stop spying on me!”: Speech privacy protection against voice assistants. In: ACM International Conference on Embedded Networked Sensor Systems (2020)
Jinci Technologies: Waterproof Ultrasonic Sensor. http://www.jinci.cn/index.php?c=article&a=type&tid=16. Accessed 7 July 2021
Siglent Technologies: Sdg1000 series function/arbitrary waveform generators. https://www.siglenteu.com/waveform-generators. Accessed 7 July 2021
Westervelt, P.J.: The theory of steady forces caused by sound waves. J. Acoust. Soc. Am. 23(3), 312–315 (1951)
Xu, C., Li, Z., Zhang, H., Rathore, A.S., Li, H., Song, C., Wang, K., Xu, W.: Waveear: Exploring a mmwave-based noise-resistant speech sensing for voice-user interface. In: International Conference on Mobile Systems, Applications, and Services (2019)
Yoneyama, M., Fujimoto, J., Kawamo, Y., Sasabe, S.: The audio spotlight: an application of nonlinear interaction of sound waves to a new type of loudspeaker design. J. Acoust. Soc. Am 73(5), 1532–1536 (1983)
Zhang, G., Yan, C., Ji, X., Zhang, T., Zhang, T., Xu, W.: Dolphinattack: Inaudible voice commands. In: ACM conference on computer and communications security (2017)
This work is supported by the National Natural Science Foundation of China under Grant 61872285, the major project of the National Social Science Foundation under Grant 20ZDA062, and Center for Balance Architecture in Zhejiang University.
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
About this article
Cite this article
Chen, Y., Gao, M., Liu, Y. et al. Implement of a secure selective ultrasonic microphone jammer. CCF Trans. Pervasive Comp. Interact. (2021). https://doi.org/10.1007/s42486-021-00074-2
- Privacy protection
- Nonlinear effects
- Selective jamming