Existing acoustic indoor location sensing systems are limited by the need for extensive data collection and model retraining in unseen environments. This paper introduces ZA-SLAM, a novel zero-shot acoustic Simultaneous Localization and Mapping (SLAM) system that can be deployed in unseen environments without model retraining. Our core idea is to train an acoustic encoder that inherits the generalization capabilities of pre-trained Vision-Language Models (VLMs), which show superiority in tasks like zero-shot visual SLAM. To achieve this goal, we perform Acoustic-Visual Feature Alignment to enable the acoustic encoder to generate features aligned with visual features from VLMs. To select high-quality images for effective alignment, we design a Semantic-Guided Image Selection that filters out low-quality collected images caused by factors like abrupt view changes, occlusions, and uninformative views. Furthermore, we address the challenge of false positive loop closures in structurally similar locations with the Learning-Based Trajectory Reachability Matching that validates loop closures leveraging IMU trajectory features. Extensive real-world experiments demonstrate that our system achieves comparable SLAM performance to retraining-based acoustic SLAM, and much improved performance compared to existing zero-shot Wi-Fi and geomagnetic SLAM systems. Our system achieves a mean mapping error of 0.56 m and a localization error of 0.78 m across multiple unseen environments.

Online voice communications are widely used nowadays. To protect speech from leakage, people tend to initiate the talk in sound-isolated environments. In this paper, we reveal a novel attack that recovers high-quality speech from outside soundproof zones. The rationale of the attack is to leverage sound-sensitive characteristics of piezoelectric materials, i.e., a piezo film that can change the phase of reflected mmWaves when placed in a sound field. If the attacker transmits mmWaves and analyzes reflected signals from the piezo film, the speech information can be compromised. More importantly, the piezo film is paper-like and works without a power supply. We propose a new speech recovery methodology to transform sound waves into wireless signals and build an end-to-end eavesdropping system working as a through-wall “microphone” to recover high-quality speech stealthily. To combat signal attenuation and improve speech quality, we develop a speech-enhancement scheme based on generative adversarial networks and propose to use multi-antenna information for intelligible speech reconstruction. We conduct extensive experiments to evaluate the system. The results indicate that the system achieves over 98% accuracy for digit recognition and works well over 5m away through the wall. We also test the system under complex scenarios and give countermeasures.

Device fingerprints can satisfy the high-security requirement of modern mobile applications (e.g., mobile payments) by guaranteeing the operation is performed on a trusted device. However, existing works on device fingerprints are weak to leakage, which leads to an irreversible failure of the device fingerprint authentication system after suffering from fingerprint theft attacks. The vulnerability drives us to propose a reconfigurable device fingerprint, i.e.,MotoPrint, that can recover the system after suffering from such attacks.MotoPrintstems from the motor vibration that can represent in both signals of the accelerometer and the gyroscope (i.e., they are homologous motion signals). Therefore, we designed a two-path feature extracting network and a sensor-independent training strategy to eliminate sensor noise that can decline authentication performance. In addition,MotoPrinthas a complete reconfiguration mechanism to cope with fingerprint leakage, which brings the damaged authentication system back to health. The evaluation of 80 stand-alone vibration motors and 20 in-built ones shows thatMotoPrintcan achieve high authentication accuracy of 98.5%. Meanwhile, we also demonstrate the reconfiguredMotoPrint, which can also effectively indicate the device's uniqueness with over 98% accuracy, is independent ofMotoPrints under other stimulating codes.

Fingerprint recognition has been a vital security guard for various applications whose vulnerability has been explored by different works. However, previous works on spoofing fingerprint recognition rely on prior knowledge (e.g., photos and minutiae) of the target fingerprint, which fails to implement in practical scenarios. In this paper, we design a fingerprint spoofing attack, namely FingerFaker, to explore the vulnerability of fingerprint recognition, which can spoof automated fingerprint recognition systems (AFRSs) without prior knowledge of target fingerprints. Specifically, we propose a novel concept of "pseudo-minutiae-set" as an effective optimization object and design a two-stage scheme to optimize "pseudo-minutiaeset" leveraging a two-factor evolutionary strategy. In addition, we use a GAN-based training strategy with a minutiae loss function to pre-train a fingerprint generator to map a "pseudo-minutiae-set" into a fingerprint. We use 6342 fingerprint images to verify the performance of FingerFaker on spoofing the open-source AFRS, which shows a high attack success rate (ASR) of 97.78%. Meanwhile, we conduct a realistic case study on commercial off-the-shelf (COTS) AFRS, where FingerFaker also shows 94.22% ASR. Finally, we explore the impact of different conditions to guide the attack and propose countermeasures to mitigate the harm.

More and more people turn to online voice communication with loudspeaker-equipped devices due to its convenience. To prevent speech leakage, soundproof rooms are often adopted. This paper presents mmPhone, a novel acoustic eavesdropping system that recovers loudspeaker speech protected by soundproof environments. The key idea is that properties of piezoelectric films in mmWave band can change with sound pressure due to the piezoelectric effect. If the property changes are acquired by an adversary (i.e., characterizing the piezoelectric effect with mmWaves), speech leakage can happen. More importantly, the piezoelectric film can work without a power supply. Base on this, we proposed a methodology using mmWaves to sense the film and decoding the speech from mmWaves, which turns the film into a passive "microphone". To recover intelligible speech, we further develop an enhancement scheme based on a denoising neural network, multi-channel augmentation, and speech synthesis, to compensate for the propagation and penetration loss of mmWaves. We perform extensive experiments to evaluate mmPhone and conduct digit recognition with over 93% accuracy. The results indicate mmPhone can recover high-quality and intelligible speech from a distance over 5m and is resilient to incident angles of sound waves (within 55 degrees) and different types of loudspeakers.

MapReduce-based SQL processing frameworks, such as Hive and Spark SQL, are widely used to support big data analytics. Currently these systems mainly adopt the record-at-a-time execution model, which is less efficient in terms of CPU utilization. In contrast, vectorized execution is able to make better use of CPU cache by bulk processing a record batch at a time. However, simply applying vectorized execution to MapReduce-based frameworks results in low efficient vectorized shuffle. Moreover, existing vectorized execution donot make full use of CPU cache for complex operators (e.g. Sort and Aggregation). In this paper, we present VEE, a thorough vectorized execution engine designed for SQL query processing on Spark. First, VEE designs compact in-memory data layout and serialization-aware assembling for vectorized shuffle to expedites shuffle execution, since they reduce shuffle data footprint and related computations. Secondly, VEE applies in-memory record batch rearrangement for Sort and Aggregation to greatly reduce random memory access and increase query performance. Thirdly, VEE carefully designs operator-aware batch length when handling different operators, which makes better utilization of CPU cache and increases query performance. We conduct extensive performance evaluations. The experiment results show that the performance speedup of VEE against Spark is up to 72.7% and 25.0% on average for OLAP workloads (TPC-H). The vectorized execution technologies in VEE are also applicable to other MapReduce-based data analytic frameworks to improve their query performance.

Speaker verification system has gained great popularity in recent years, especially with the development of deep neural networks and Internet of Things. However, the security of speaker verification system based on deep neural networks has not been well investigated. In this paper, we propose an attack to spoof the state-of-the-art speaker verification system based on generalized end-to-end (GE2E) loss function for misclassifying illegal users into the authentic user. Specifically, we design a novel loss function to deploy a generator for generating effective adversarial examples with slight perturbation and then spoof the system with these adversarial examples to achieve our goals. The success rate of our attack can reach 82% when cosine similarity is adopted to deploy the deep-learning-based speaker verification system. Beyond that, our experiments also reported the signal-to-noise ratio at 76 dB, which proves that our attack has higher imperceptibility than previous works. In summary, the results show that our attack not only can spoof the state-of-the-art neural-network-based speaker verification system but also more importantly has the ability to hide from human hearing or machine discrimination.

MapReduce-based SQL processing systems, e.g., Hive and Spark SQL, are widely used for big data analytic applications due to automatic parallel processing on large-scale machines. They provide high processing performance when loads are balanced across the machines. However, skew loads are not rare in real applications. Although many efforts have been made to address the skew issue in MapReduce-based systems, they can neither fully exploit all available computing resources nor handle skews in SQL processing. Moreover, none of them can expedite the processing of skew partitions in case of failures. In this paper, we present SrSpark, a MapReduce-based SQL processing system that can make full use of all computing resources for both non-skew loads and skew loads. To achieve this goal, SrSpark introduces fine-grained processing and work-stealing into the MapReduce framework. More specifically, SrSpark is implemented based on Spark SQL. In SrSpark, partitions are further divided into sub-partitions and processed in sub-partition granularity. Moreover, SrSpark adaptively uses both intra-node and inter-node parallel processing for skew loads according to available computing resources in realtime. Such adaptive parallel processing increases the degree of parallelism and reduces the interaction overheads among the cooperative worker threads. In addition, SrSpark checkpoints sub-partition's processing results periodically to ensure fast recovery from failures during skew partition processing. Our experiment results show that for skew loads, SrSpark outperforms Spark SQL by up to 3.5x, and 2.2x on average, while the performance overhead is only about 4% under non-skew loads.

A trend in nowadays data centers is that heterogeneous storage devices are deployed to meet different storage demands of various big data workloads. For example, many nodes are equipped with both SSDs and HDDs. And HDFS has introduced the heterogeneous-storage-aware feature to adapt to such hybrid storage clusters. However, current task scheduler on big data processing platforms (such as Hadoop and Spark) only considers the overhead of network data transmission by exploiting the data locality principle. On heterogeneous storage clusters, task completion time is also affected by the speed of storage devices (SSDs and HDDs) where the data are stored. Ignoring the different speed of storage devices results in poor utilization of high speed devices such as SSD. In this paper, we propose a task scheduling strategy for heterogeneous storage clusters called H-Scheduler. The key idea of H-Scheduler is to differentiate speeds of storage devices by storage types. It classifies the tasks by both data locality and storage types, and redefines the priorities of different classes of tasks by both storage device speed and data locality to reduce job execution time. We implemented H-Scheduler in Spark, and the experiment results show that H-Scheduler can reduce job execution time by up to 73.6 %, depending on the workload characteristics and data distribution among different types of storage devices.