As generative audio models are rapidly evolving, AI-generated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on state-of-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

Deep learning and computer vision have experienced significant advancements, particularly in critical applications such as autonomous driving and real-time surveillance, where object detection (OD) plays a pivotal role. Ensuring the accuracy and speed of these systems is paramount to prevent accidents or failures. Recently, latency-based attacks have emerged as a new threat, driven by the essential need for real-time performance in various applications. These attacks target model responsiveness to disrupt system performance without necessarily compromising accuracy. Our preliminary experiments show that introducing just a few bit flips to key parameters in OD models can significantly increase latency, degrading performance. Meanwhile, recent advancements in memory-based attacks, such as Row Hammer [18], demonstrate the ability to conveniently introduce bit flips at desired locations without physical hardware interaction. Based on the observations, we propose a novel attack on OD models that leverages row-hammer to introduce bit-flips via side channels, targeting the non-maximum suppression (NMS) filter and significantly increasing latency. Unlike previous methods that modify input data, our technique ensures efficiency by minimizing bit-flips through critical path exploitation and achieves practical applicability with only a subset of validation data. Experiments across various datasets and models validate our approach, demonstrating latency increases up to 71.6 ms (20.4×) with just 31 bit-flips.

Recent advances in speech and language processing have led to the rise of smart voice services like Alexa, Google Home, and Siri. However, these advancements also increase security risks due to sophisticated voice domain attacks. Instead of relying on acoustic clues to detect replayed or synthesized speech, we utilize microphones and motion sensors in head-wearable devices to authorize legitimate users through bone-conducted vibrations, enabling multi-factor authentication (MFA) for spoken voice. Our proposed two-stage authentication system, AirBone, captures air and bone conduction (AirBone) signals and exploits two authentication factors sequentially. The first stage, called temporal consistency scoring (TCS), employs signal processing to verify the recorded AC and BC signals are concurrent and originate from the same vocalization process. Statistical tools are employed to distinguish legitimate attempts against false-triggering or acoustic attacks. The second stage leverages deep learning to verify the user’s unique bone conduction patterns in the vibration domain. Specifically, we enhance the robustness through data augmentation with constant-Q transform and adversarial training, improving the model’s ability to detect impersonation and machine-induced vibrations. Thanks to these designs, AirBone authentication offers enhanced security via MFA with no extra cost of user effort. In addition, our experimental results demonstrate a$96.3\%$overall accuracy, robustness against AirBone noise and room impulse responses, and$0.3\%$Equal Error Rate (EER) against acoustic and cross-domain attacks.

The data and power transfer systems for long term underground CO2 sequestration monitoring are normally based on wire-line cable, which will lead to a potential leakage path way through the casing and cement annulus in high-temperature, high-pressure, and hash underground environments. In this paper, a novel wireless power transfer system has been developed for real-time underground CO2 monitoring. The system includes an array of toroidal transceivers winding around the highly conductive casing string for wireless power transfer to deep subsurface. This design helps to maintain well integrity and reduce potential leakage by eliminating the need to perforate the casing or an umbilical in the cement annulus. The metal casing’s amplification effect significantly enhances the wireless power transfer efficiency, which provides a highly conductive power/electric current’s pathway instead of omnidirectional wireless radiation loss in the subsurface. Toroidal transceiver’s design has been optimized to improve the received signal, and our results show significant improvements in wireless power transfer efficiency. Using the optimized design, we can receive 1 to 10 % power transfer efficiency at 800 meters deep using only one toroidal transceiver with 1A current as input. Compared with other wireless antenna designs, such as the helix coil antenna, our system has shown 26,000 times power transfer efficiency improvement. In the end, a lab-scale power transfer system is built, and our experimental measurements support the simulation results.

This artilce aims to develop a differential private federated learning (FL) scheme with the least artificial noises added while minimizing the energy consumption of participating mobile devices. By observing that some communication efficient FL approaches and even the nature of wireless communications contribute to the differential privacy (DP) preservation of training data on mobile devices, in this paper, we propose to jointly leverage gradient compression techniques (i.e., gradient quantization and sparsification) and additive white Gaussian noises (AWGN) in wireless channels to develop a piggyback DP approach for FL over mobile devices. Even with the piggyback DP approach, information distortion caused by gradient compression and noise perturbation may slow down FL convergence, which in turn consumes more energy of mobile devices for local computing and model update communications. Thus, we theoretically analyze FL convergence and formulate an energy efficient FL optimization under piggyback DP, transmission power, and FL convergence constraints. Furthermore, we propose an efficient iterative algorithm where closed-form solutions for artificial DP noise and power control are derived. Extensive simulation and experimental results demonstrate the effectiveness of the proposed scheme in terms of energy efficiency and privacy preservation.

Federated learning (FL) enables mobile devices to collaboratively learn a shared prediction model while keeping data locally. However, there are two major research challenges to practically deploy FL over mobile devices: (i) frequent wireless updates of huge size gradients v.s. limited spectrum resources, and (ii) energy-hungry FL communication and local computing during training v.s. battery-constrained mobile devices. To address those challenges, in this paper, we propose a novel multi-bit over-the-air computation (M-AirComp) approach for spectrum-efficient aggregation of local model updates in FL and further present an energy-efficient FL design for mobile devices. Specifically, a high-precision digital modulation scheme is designed and incorporated in the M-AirComp, allowing mobile devices to upload model updates at the selected positions simultaneously in the multi-access channel. Moreover, we theoretically analyze the convergence property of our FL algorithm. Guided by FL convergence analysis, we formulate a joint transmission probability and local computing control optimization, aiming to minimize the overall energy consumption (i.e., iterative local computing + multi-round communications) of mobile devices in FL. Extensive simulation results show that our proposed scheme outperforms existing ones in terms of spectrum utilization, energy efficiency, and learning accuracy.

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

In-pipe communication exists in broad applications such as structural health monitoring, gas and oil exploration, and remote sensing. The widely deployed pipeline infrastructure provides a new medium to realize wireless communications in the harsh environment, especially underwater or underground communications. The high frequency (above 80 kHz) reverberating stress wave in a short range allows piezoelectric transducers to transmit information. This paper investigates stress wave propagation, the reverberating stress wave channel characteristics, and several potential techniques for the reverberating stress wave channel. Based on our experimental and simulation results, single-input-multiple-output orthogonal frequency-division multiplexting (SIMO-OFDM) and uplink single-carrier FDMA are considered the most suitable schemes for reverberating stress wave communications in point-to-point and multi-user scenarios, respectively. Our evaluation results show the spectral efficiency upper bound is 3.18 bit/s/Hz for 1×3 SIMO and the rate-sum capacity for uplink SC-FDMA is 75.18 kb/s with 30 kHz total bandwidth.

Leveraging the mobility of autonomous underwater vehicles (AUVs) to collect and deliver data among different underwater devices enables numerous underwater Internet-of-Things (UW-IoT) applications. However, the most versatile underwater acoustic communications (UACs) may not be suitable in the AUV-assisted UW-IoT scenarios, considering the high cost and high power consumption of acoustic transducers, as well as high error rates of UACs due to the complex underwater acoustic channel conditions. Alternatively, we propose to apply the low-power magnetic induction (MI)-based wireless communications for AUV data dissemination and collection. Due to the mobility of AUVs and the underwater turbulence, MI channels between AUVs and other underwater devices are no longer stable and static, which poses great challenges to establish reliable MI links. To tackle this problem, we investigate the dynamic MI wireless communications in this article. We first mathematically characterize the dynamic MI channel when an AUV approaches its target for data collection. Based on this dynamic channel model, the dynamic communication range and available bandwidth of MI are derived. We also build an MI wireless communication system that can work within a dynamic range. The communication performances are evaluated through numerical simulations as well as underwater experiments.