There is an emerging trend of using deep learning (DL) to handle complex tasks in wireless communication systems. However, recent research suggests that DL-enabled communication systems are vulnerable to adversarial attacks. Fortunately, most of these attacks are simulation-based, incapable of handling realistic channels with, e.g., multipath fading, temporal dynamics, and hardware nonlinearity, and hence lack of practicality. To this end, we present RadioShock, an over-the-air adversarial attack against wireless communication systems. Through accurate estimations of channel states for dynamic adaptations, RadioShock is made for real-world communication scenarios.We further introduce a universal compact perturbation generation algorithm, along with a new perturbation constraint strategy, aiming to achieve covert over-the-air adversarial attacks. We implement a RadioShock prototype and conduct extensive experiments using automatic modulation classification systems as a representative application scenario. The results reveal that RadioShock diminishes the accuracy of diverse models utilized in wireless communication systems by up to 52.41%, far more effective than existing simulation-based adversarial attacks in facing real-world applications.

Acoustic eavesdropping against private or confidential spaces is a significant threat in the realm of privacy protection. While the presence of soundproof material would weaken such an attack, current eavesdropping technology may be able to bypass these protections. Fortunately, existing studies either inadequately cover the full spectrum of human speech due to low-frequency responses or rely heavily on the prior knowledge used to train a model. To address these challenges, this paper introduces mmEcho, a new acoustic eavesdropping method that utilizes millimeter-wave signals to sense vibration induced by sound precisely. Through signal processing techniques such as the intra-chirp scheme and phase calibration algorithm, mmEcho achieves micrometer-level vibration extraction without requiring target-related data. To improve the range of eavesdropping attacks while reducing noise, we optimize radar signals by leveraging the widespread availability of multiple antennas on commercial off-the-shelf radars. We comprehensively evaluate the performance of mmEcho in different real-world settings. Experimental results demonstrate that, with the aid of multi-antenna technology, mmEcho can more effectively reconstruct the audio from the target at various distances, directions, sound insulators, reverberating objects, sound levels, and languages. Compared to existing methods, our approach provides better effectiveness without prior knowledge, such as the speech data from the target.

In today’s information age, eavesdropping has been one of the most serious privacy threats in information security, such as exodus spyware (Rudie et al., 2021) and pegasus spyware (Anatolyevich, 2020). And the main one of them is acoustic eavesdropping. Acoustic eavesdropping (George and Sagayarajan, 2023) is a technology that uses microphones, sensors, or other devices to collect and process sound signals and convert them into readable information. Although much research has been done in this area, there is still a lack of comprehensive investigation into the timeliness of this technology, given the continuous advancement of technology and the rapid development of eavesdropping methods. In this article, we have given a selective overview of acoustic eavesdropping, focusing on the methods of acoustic eavesdropping. More specifically, we divide acoustic eavesdropping into three categories: motion sensor-based acoustic eavesdropping, optical sensor-based acoustic eavesdropping, and RF-based acoustic eavesdropping. Within these three representative frameworks, we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of each. Secondly, we also introduce several important but challenging applications of these acoustic eavesdropping methods. In addition, we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives, including whether they are non-intrusive, whether they can achieve unconstrained word eavesdropping, and whether they use machine learning, etc. The general template of our article is as follows: firstly, we systematically review and classify the existing eavesdropping technologies, elaborate on their working mechanisms, and give corresponding formulas. Then, these eavesdropping methods were compared and analyzed, and each method’s effectiveness and technical difficulty were evaluated from multiple dimensions. In addition to an assessment of the current state of the field, we discuss the current shortcomings and challenges and give a fruitful direction for the future of acoustic eavesdropping research. We hope to continue to inspire researchers in this direction.

As acoustic communication systems become increasingly common in our daily life, eavesdropping brings severe security and privacy risks. Current methods of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words based on classification approaches, or cannot work through-wall because of the use of optical sensors. In this article, we presentmilliEar, a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio.milliEarcombines speaker vibration estimation with conditional generative adversarial networks to eavesdrop and recover high-quality audios (i.e., with no vocabulary constraints). We implement and evaluatemilliEarusing off-the-shelf mmWave radars deployed in different scenarios and settings. Evaluation results clearly show thatmilliEarcan accurately reconstruct the audio even at different distances, angles, and through the wall with different insulator materials. In addition, our subjective and objective evaluations demonstrate that the reconstructed audio has a strong similarity with the original audio.

Acoustic eavesdropping targeting private or confidential spaces is one of the most severe privacy threats. Soundproof rooms may reduce such risks, but they cannot prevent sophisticated eavesdropping, which has been an emerging research trend in recent years. Researchers have investigated such acoustic eavesdropping attacks via sensor-enabled side-channels. However, such attacks either make unrealistic assumptions or have considerable constraints. This paper introduces mmEcho, an acoustic eavesdropping system that uses a millimeter-wave radio signal to accurately measure the micrometer-level vibration of an object induced by sound waves. Compared with previous works, our eavesdropping method is highly accurate and requires no prior knowledge about the victim. We evaluate the performance of mmEcho under extensive real-world settings and scenarios. Our results show that mmEcho can accurately reconstruct audio from moving sources at various distances, orientations, reverberating objects, sound insulators, spoken languages, and sound levels.