Deep learning technologies have seen widespread adoption in WiFi-based wireless sensing systems. However, they are inherently vulnerable to adversarial perturbation attacks, which has received little attention within the WiFi sensing community. To more comprehensively understand the potential threats posed by perturbation attacks, we present a novel attack method, named WiIntruder, distinguishing itself with universality, robustness, and stealthiness. This paper intends to provide a catalyst that promotes the assessment of security in existing WiFi-based sensing systems. We achieve the three aforementioned salient features in WiIntruder through the following three steps: (1) Maximizing transferability by differentiating user-state-specific feature spaces across sensing models, thereby enabling a universal perturbation attack vector applicable to a wide range of applications; (2) Mitigating the impact of perturbation signal distortion by optimizing key factors of device synchronization and wireless propagation through a heuristic particle swarm algorithm; and (3) Enhancing the diversity and stealthiness of attack patterns by randomly switching among perturbation surrogates generated by a generative adversarial network. Experimental results confirm the threat posed by WiIntruder to four common WiFi-based services, with the average accuracy decrease by 72.9% under black-box attack scenarios.

In recent years, video steganography technique based on H.265/HEVC has received widespread attention. Typically, video steganography selects various syntax elements during the encoding process as carriers, and utilizing Prediction Unit (PU) as carrier is currently one of the most significant research directions. However, due to the limited number of PU types, such algorithms often suffer from insufficient capacity and visual quality. To alleviate the aforementioned issues, this paper proposes an H.265/HEVC video steganography algorithm that utilizes polygon encoding and Improved Deep Learnable Similarity Network Filter (IDLSNF). Firstly, we design a new polygon encoding rule, which maps different integers into several polygons. Secondly, we propose a novel steganography method based on polygon encoding and PU partition mode. This method selects the PUs of$8\times 8$and$16\times 16$coding unit in P-frames as carriers and hides the secret message by modifying the partition mode of two adjacent PUs. Due to the ability of polygon encoding to represent more information within a small range, it increases capacity with low steganographic distortion. Thirdly, we further propose a filter by improving DLSN, which enhances the visual quality of the entire stego video by processing I-frames. Extensive experimental results show that the video steganography algorithm proposed in this paper achieves higher capacity and superior visual quality compared to current State-of-the-Art methods. Meanwhile, our algorithm can also obtain good BRI and anti-steganalysis performance. This method has promising application prospects in the field of video covert communication.

Voice-enabledmobile applications(apps) are exploding in popularity as they could be manipulated with voice commands to achieve convenient man-machine interaction. These voice-enabled apps also raise security and privacy concerns about whether they would maliciously invoke microphones to realize voice eavesdropping. To explore this issue, in this work, we design baleful apps to access the microphone covertly, the results of test studies demonstrate that covert eavesdropping attacks can bypass existing device detection schemes as well as are unnoticeable to human users. To prevent the covert voice eavesdropping attack, we propose a versatilemicrophone icon detection(MicID) scheme inspired by the groundtruth that authorization of the voice function requires the user to touch the specific microphone icon in most of voice-based apps. Specifically, we devise a deep learning model,lightweight YOLO(L-YOLO), to locate the microphone icon on the screen quickly and accurately. By determining whether the located microphone icon is touched by the user, we can judge whether the current microphone access belongs to the app's normal operation or illegal eavesdropping. Finally, we conduct extensive experiments by deploying the scheme on real devices and collecting dataset. The evaluation results show that the proposed MicID scheme achieves more than 99% accuracy with low computation cost.

The mobile health (mHealth) system, leveraging mobile edge computing, can monitor health status and provide diagnosis. However, due to the privacy of medical data and the resource limitations of mobile devices, patients are unable to access diagnostic services provided by untrusted servers in real-time. Existing schemes present significant challenges in private heterogeneous data aggregation, model training and inference in the presence of malicious participants, and expensive resource consumption. To address these issues, in this paper, we propose a non-interactive privacy-preserving system with the naive Bayesian model, i.e.,$\mathsf {RobustHealth}$, for heterogeneous mHealth diagnosis. Specifically, we extract homogeneous features from heterogeneous datasets to enable efficient encrypted aggregation. We propose a novel private model training algorithm with enhanced security to against collusion-then-differential attacks. We develop a novel non-interactive private model inference algorithm using minimal lightweight cryptographic primitives, designed for patients under unstable network environments. We provide formal security proofs for our system using the Universal Composable (UC) framework. To validate the performance of$\mathsf {RobustHealth}$, we conduct extensive experiments on real-world heterogeneous datasets, and compared with related works. The results demonstrate a$\bf {4.37\%}$improvement in model accuracy, along with significant reductions in computational and communication overheads of$\bf {21.18\times }$and$\bf {4.24\times }$, respectively.

Voice assistants have been widely adopted for their ability to provide non-touch human-computer interaction. However, while they offer convenience, their continuous listening for specific wake-up words raises privacy concerns, as it may lead to eavesdropping on user conversations. To investigate this issue, we devised covert eavesdropping attacks by perturbing and replaying events generated during the user’s normal activation of the voice assistant. The results demonstrate the feasibility and harmfulness of such eavesdropping attacks. To counter these covert voice eavesdropping attacks, we propose an effective defense scheme called CrossUnwind. This scheme leverages the groundtruth that voice assistant wake-up requires hardware to generate and send wake-up events. Specifically, we designed a novel tombstone file parsing process and an accurate event discrimination algorithm to obtain detailed call station information of the wake-up event without compromising the system. This allows us to determine whether the current wake-up event was generated by hardware. We deployed CrossUnwind on real devices and compared it to well-known machine learning and deep learning methods. The results demonstrate that CrossUnwind can achieve high accuracy in eavesdropping detection with faster speeds and lower resource utilization.

Butterfly counting is widely used to analyze bipartite networks, but counting butterflies in original bipartite networks can reveal sensitive data and pose a risk of individual privacy, specifically edge privacy. Current privacy notions do not fully address the needs of both user-user and user-item bipartite networks. In this paper, we propose a novel privacy notion, edge decentralized differential privacy (edge DDP), which preserves edge privacy in any bipartite network. We also design the randomized edge protocol (REP) to perturb real edges in bipartite networks. However, a significant amount of noise in perturbed bipartite networks often leads to an overcount of butterflies. To achieve accurate butterfly counting, we design the randomized group protocol (RGP) to reduce noise. By combining REP and RGP, we propose a two-phase framework called butterfly counting in limitedly synthesized bipartite networks (BC-LimBN) to synthesize networks for accurate butterfly counting. BC-LimBN has been rigorously proven to satisfy edge DDP. Our experiments on various datasets confirm the high accuracy of BC-LimBN in butterfly counting and its superiority over competitors, with a mean relative error of less than 10% at most. Furthermore, our experiments show that BC-LimBN has a low time cost, requiring only a few seconds on our datasets.

Numerous mobile devices are equipped with voice assistants to facilitate contactless user-device interaction. However, the widespread availability of voice assistants also raises security and privacy concerns, as they can be maliciously triggered to perform voice eavesdropping. Although diverse attacks have been taken to manipulate voice assistants for eavesdropping, they exhibit deficiencies of limited attack scopes and conspicuous attack behaviors because they target specific voice assistants or require extra voice commands to activate them. To manipulate arbitrary voice assistants for covert eavesdropping attack, we conduct a comprehensive analysis of voice assistant implementation in the Android system and refine a universal workflow. Through meticulous analysis and experimental verification, we uncover an inherent vulnerability that in voice assistants across device types that can be awakened by an artificial faking Intent. Building on this significant discovery, we propose an attack termed VoiceEar. It leverages a malicious event generation file and a first-in-first-out Intent generation algorithm to trigger voice assistants within the normal workflow for eavesdropping, without voice commands. Finally, we deploy the VoiceEar attacks on 25 mainstream mobile devices, and invite 95 volunteers for eavesdropping activity perception testing. The results unequivocally demonstrate the seamless execution of VoiceEar attacks, with neither users nor devices awareness.

Microphone has been widely integrated into mobile devices to provide physical basis for human-device voice interaction. However, the microphone may be spitefully invoked by maliciousmobile applications(apps) with arousing security and privacy concerns. In this work, to explore the issue of illegal microphone access, we develop spiteful apps through native and injection development to access the microphone viciously on a series of mobile devices. The results demonstrate that baleful apps could enable the microphone arbitrarily without any hint. To combat the unauthorized microphone access behavior, we design amicrophone illegal access detection(MicDet) scheme by constructing a request-response time model using the Unix time stamps of voice icon touched and microphone invoked. Through conducting numerical analysis and hypothesis testing to effectively verify the request-response pattern of app's normal access, we detect illegal access by analyzing whether the touch operation matches the normal pattern. For friendly user experience, we design an intuitive floating window to alert users by displaying the name of the app that illegally accessed the microphone once the illegal behavior is detected. Finally, we apply our scheme to different mobile devices and test several apps, the experimental results show that the MicDet scheme achieves a high detection accuracy.

Millions of mobile devices are currently equipped withvoice assistant(VA) for robust identity authentication. Regrettably, VA authentication remains susceptible to voice spoofing attacks, encompassing playback, synthesis, and conversion attacks. Despite numerous proposed defense schemes, these solutions exhibit deficiencies such as limited versatility and cumbersome implementation. Many are specialized in detecting only one specific type of attack, necessitate additional equipment, or mandate placing the device in specific locations. In this study, we introduce a versatile and user-friendly scheme designed to counteract voice spoofing attacks by analyzing common nonlinear features inherent in vocalization systems. Initially, we demonstrate the nonlinear nature of both human and mobile device vocalization by scrutinizing the mechanisms and processes of voice generation. Subsequently, we develop a comprehensive nonlinear model and extract a universal acoustic nonlinear property to discern sounds produced by humans from those generated by loudspeakers, thereby enhancing resistance against spoofing attacks. Finally, we conduct extensive experiments utilizing a real-world collected dataset and the supplementary ASVspoof2017 dataset. Evaluation results reveal that the proposed scheme significantly improves accuracy and computation cost by nearly 40% and 15%, respectively.

Enormous mobile applications (apps) now support voice functionality for convenient user-device interaction. However, these voice-enabled apps may spitefully invoke microphone to realize voice eavesdropping with arousing security risks and privacy concerns. To explore the issue of voice eavesdropping, in this work, we first design eavesdropping apps through native development and injection development to conduct eavesdropping attacks on a series of smart devices. The results demonstrate that eavesdropping could be carried out freely without any hint. To thwart voice eavesdropping, we propose a valid eavesdropping detection (EarDet) scheme based on the discovery that the activation of voice function in most apps requires authorization from the user by touching a specific voice icon. In the scheme, we construct a request-response time model using the Unix time stamps of touching the voice icon and microphone invoked. Through numerical analysis and hypothesis testing to effectively verify the pattern of the app’s normal access under user authorization to the microphone, we could detect eavesdropping attacks by sensing whether there is a touch operation. Finally, we apply the scheme to different smart devices and test several apps. The experimental results show that the proposed EarDet scheme can achieve a high detection accuracy.

Bothvoice communicationand automatic speech verification (ASV) over smart devices are vulnerable to the voice impersonation (VI) attack, which is often launched via imitating a target’s voice characteristics to deceive human auditory sense or fool the ASV system. Researchers have designed a number of defense schemes yet without the consideration of universality due to the lack of comprehensive data sets. In this article, we propose a universal defense scheme based on the VI data set collected from a famous TV show named “The Sound.” First, we deliver a thorough study on the VI attacks in both auditory and ASV systems to verify the collected simulated voice could spoof the auditory and the ASV system with a notable probability. Second, we propose a quasi-Gaussian distribution (QGD)-based defense scheme with the discovery about specific voice characteristics that are distinct between attackers and targets. Finally, we conduct extensive experimental results on our collected VI data set as well as the auxiliary ASVspoof2017 data set, to indicate the proposed QGD scheme outperforms the state-of-the-art schemes: backpropagation neural network, support vector machine, and Gaussian mixture model, in terms of accuracy.