arXiv subjects

Qi Li

Publications and source records attributed to Qi Li.

At least 271 records · Page 15Linked to original sources

arXiv2024

Zero-Query Adversarial Attack on Black-box Automatic Speech Recognition Systems

In recent years, extensive research has been conducted on the vulnerability of ASR systems, revealing that black-box adversarial example attacks pose significant threats to real-world ASR systems. However, most existing black-box attacks rely on queries to the target ASRs, which is impractical when queries are not permitted. In this paper, we propose ZQ-Attack, a transfer-based adversarial attack on ASR systems in the zero-query black-box setting. Through a comprehensive review and categorization of modern ASR technologies, we first meticulously select surrogate ASRs of diverse types to generate adversarial examples. Following this, ZQ-Attack initializes the adversarial perturbation with a scaled target command audio, rendering it relatively imperceptible while maintaining effectiveness. Subsequently, to achieve high transferability of adversarial perturbations, we propose a sequential ensemble optimization algorithm, which iteratively optimizes the adversarial perturbation on each surrogate model, leveraging collaborative information from other models. We conduct extensive experiments to evaluate ZQ-Attack. In the over-the-line setting, ZQ-Attack achieves a 100% success rate of attack (SRoA) with an average signal-to-noise ratio (SNR) of 21.91dB on 4 online speech recognition services, and attains an average SRoA of 100% and SNR of 19.67dB on 16 open-source ASRs. For commercial intelligent voice control devices, ZQ-Attack also achieves a 100% SRoA with an average SNR of 15.77dB in the over-the-air setting.

cs.CR↗

arXiv2024

Artificial Immune System of Secure Face Recognition Against Adversarial Attacks

Insect production for food and feed presents a promising supplement to ensure food safety and address the adverse impacts of agriculture on climate and environment in the future. However, optimisation is required for insect production to realise its full potential. This can be by targeted improvement of traits of interest through selective breeding, an approach which has so far been underexplored and underutilised in insect farming. Here we present a comprehensive review of the selective breeding framework in the context of insect production. We systematically evaluate adjustments of selective breeding techniques to the realm of insects and highlight the essential components integral to the breeding process. The discussion covers every step of a conventional breeding scheme, such as formulation of breeding objectives, phenotyping, estimation of genetic parameters and breeding values, selection of appropriate breeding strategies, and mitigation of issues associated with genetic diversity depletion and inbreeding. This review combines knowledge from diverse disciplines, bridging the gap between animal breeding, quantitative genetics, evolutionary biology, and entomology, offering an integrated view of the insect breeding research area and uniting knowledge which has previously remained scattered across diverse fields of expertise.

cs.CV↗

arXiv2024

Boosting the convergence of DSMC by GSIS

A deterministic-stochastic coupling scheme is developed for simulating rarefied gas flows, where the key process is the alternative solving of the macroscopic synthetic equations [Su et al., J. Comput. Phys., 407 (2020) 109245] and the mesoscopic equation via the asymptotic-preserving time-relaxed Monte Carlo scheme [Fei, J. Comput. Phys., 486 (2023) 112128]. Firstly, the macroscopic synthetic equations are exactly derived from the Boltzmann equation, incorporating not only the Newtonian viscosity and Fourier thermal conduction laws but also higher-order constitutive relations that capture rarefaction effects; the latter are extracted from the stochastic solver over a defined sampling interval. Secondly, the macroscopic synthetic equations, with the initial field extracted from the stochastic solver over the same sampling interval, are solved to the steady state or over certain iteration steps. Finally, the simulation particles in the stochastic solver are updated to match the density, velocity, and temperature obtained from the macroscopic synthetic equations. Moreover, simulation particles in the subsequent interval will be partly sampled according to the solutions of macroscopic synthetic equations. As a result, our coupling strategy enhances the asymptotic-preserving characteristic of the stochastic solver and substantially accelerates convergence towards the steady state. Several numerical tests are performed, and it is found that our method can reduce the computational cost in the near-continuum flow regime by two orders of magnitude compared to the direct simulation Monte Carlo method.

physics.comp-ph↗

arXiv2024

Non-perturbative dynamics of flat-band systems with correlated disorder

We develop a numerical method for the time evolution of Gaussian wave packets on flat-band lattices in the presence of correlated disorder. To achieve this, we introduce a method to generate random on-site energies with prescribed correlations. We verify this method with a one-dimensional (1D) cross-stitch model, and find good agreement with analytical results obtained from the disorder-dressed evolution equations. This allows us to reproduce previous findings, that disorder can mobilize 1D flat-band states which would otherwise remain localized. As explained by the corresponding disorder-dressed evolution equations, such mobilization requires an asymmetric disorder-induced coupling to dispersive bands, a condition that is generically not fulfilled when the flat-band is resonant with the dispersive bands at a Dirac point-like crossing. We exemplify this with the 1D Lieb lattice. While analytical expressions are not available for the two-dimensional (2D) system due to its complexity, we extend the numerical method to the 2D $α-T_3$ model, and find that the initial flat-band wave packet preserves its localization when $α= 0$, regardless of disorder and intersections. However, when $α\neq 0$, the wave packet shifts in real space. We interpret this as a Berry phase controlled, disorder-induced wave-packet mobilization. In addition, we present density functional theory calculations of candidate materials, specifically $\rm Hg_{1-x}Cd_xTe$. The flat-band emerges near the $Γ$ point ($\bf{k}=$0) in the Brillouin zone.

cond-mat.dis-nn↗

arXiv2024

I Still See You: Why Existing IoT Traffic Reshaping Fails

The Internet traffic data produced by the Internet of Things (IoT) devices are collected by Internet Service Providers (ISPs) and device manufacturers, and often shared with their third parties to maintain and enhance user services. Unfortunately, on-path adversaries could infer and fingerprint users' sensitive privacy information such as occupancy and user activities by analyzing these network traffic traces. While there's a growing body of literature on defending against this side-channel attack-malicious IoT traffic analytics (TA), there's currently no systematic method to compare and evaluate the comprehensiveness of these existing studies. To address this problem, we design a new low-cost, open-source system framework-IoT Traffic Exposure Monitoring Toolkit (ITEMTK) that enables people to comprehensively examine and validate prior attack models and their defending approaches. In particular, we also design a novel image-based attack capable of inferring sensitive user information, even when users employ the most robust preventative measures in their smart homes. Researchers could leverage our new image-based attack to systematize and understand the existing literature on IoT traffic analysis attacks and preventing studies. Our results show that current defending approaches are not sufficient to protect IoT device user privacy. IoT devices are significantly vulnerable to our new image-based user privacy inference attacks, posing a grave threat to IoT device user privacy. We also highlight potential future improvements to enhance the defending approaches. ITEMTK's flexibility allows other researchers for easy expansion by integrating new TA attack models and prevention methods to benchmark their future work.

cs.CR↗

arXiv2024

Joint Spatial-Temporal Modeling and Contrastive Learning for Self-supervised Heart Rate Measurement

This paper briefly introduces the solutions developed by our team, HFUT-VUT, for Track 1 of self-supervised heart rate measurement in the 3rd Vision-based Remote Physiological Signal Sensing (RePSS) Challenge hosted at IJCAI 2024. The goal is to develop a self-supervised learning algorithm for heart rate (HR) estimation using unlabeled facial videos. To tackle this task, we present two self-supervised HR estimation solutions that integrate spatial-temporal modeling and contrastive learning, respectively. Specifically, we first propose a non-end-to-end self-supervised HR measurement framework based on spatial-temporal modeling, which can effectively capture subtle rPPG clues and leverage the inherent bandwidth and periodicity characteristics of rPPG to constrain the model. Meanwhile, we employ an excellent end-to-end solution based on contrastive learning, aiming to generalize across different scenarios from complementary perspectives. Finally, we combine the strengths of the above solutions through an ensemble strategy to generate the final predictions, leading to a more accurate HR estimation. As a result, our solutions achieved a remarkable RMSE score of 8.85277 on the test dataset, securing \textbf{2nd place} in Track 1 of the challenge.

cs.CV↗

arXiv2024

Enhancing Multimodal Large Language Models with Multi-instance Visual Prompt Generator for Visual Representation Enrichment

Multimodal Large Language Models (MLLMs) have achieved SOTA performance in various visual language tasks by fusing the visual representations with LLMs leveraging some visual adapters. In this paper, we first establish that adapters using query-based Transformers such as Q-former is a simplified Multi-instance Learning method without considering instance heterogeneity/correlation. We then propose a general component termed Multi-instance Visual Prompt Generator (MIVPG) to incorporate enriched visual representations into LLMs by taking advantage of instance correlation between images or patches for the same sample. Quantatitive evaluation on three public vision-language (VL) datasets from different scenarios shows that the proposed MIVPG improves Q-former in main VL tasks.

cs.CV↗

arXiv2024

BlueSWAT: A Lightweight State-Aware Security Framework for Bluetooth Low Energy

Bluetooth Low Energy (BLE) is a short-range wireless communication technology for resource-constrained IoT devices. Unfortunately, BLE is vulnerable to session-based attacks, where previous packets construct exploitable conditions for subsequent packets to compromise connections. Defending against session-based attacks is challenging because each step in the attack sequence is legitimate when inspected individually. In this paper, we present BlueSWAT, a lightweight state-aware security framework for protecting BLE devices. To perform inspection on the session level rather than individual packets, BlueSWAT leverages a finite state machine (FSM) to monitor sequential actions of connections at runtime. Patterns of session-based attacks are modeled as malicious transition paths in the FSM. To overcome the heterogeneous IoT environment, we develop a lightweight eBPF framework to facilitate universal patch distribution across different BLE architectures and stacks, without requiring device reboot. We implement BlueSWAT on 5 real-world devices with different chips and stacks to demonstrate its cross-device adaptability. On our dataset with 101 real-world BLE vulnerabilities, BlueSWAT can mitigate 76.1% of session-based attacks, outperforming other defense frameworks. In our end-to-end application evaluation, BlueSWAT patches introduce an average of 0.073% memory overhead and negligible latency.

cs.CR↗

arXiv2024

Modeling and simulation of a mechanism for suppressing the flipping problem of a jumping robot

In order to solve the problem of stable jumping of micro robot, we design a special mechanism: elastic passive joint (EPJ). EPJ can assist in achieving smooth jumping through the opening-closing process when the robot jumps. First, we introduce the composition and operation principle of EPJ, and perform a dynamic modeling of the robot's jumping process. Then, in order to verify the effectiveness of EPJ in controlling the robot's smooth jump, we design a simulation experiment based on MATLAB. Through comparative experiments, it was proved that EPJ can greatly adjust the angular velocity of the robot and increase the jump distance of the robot. Finally, we analyze each parameter in EPJ and performs parameter optimization. After optimization, EPJ achieves a completely flip-free jump of the robot, laying an important foundation for improving the mobility of micro-robot.

cs.RO↗

arXiv2024

martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture

The development of machine learning models requires a large amount of training data. Data marketplaces are essential for trading high-quality, private-domain data not publicly available online. However, due to growing data privacy concerns, direct data exchange is inappropriate. Federated Learning (FL) is a distributed machine learning paradigm that exchanges data utilities (in form of local models or gradients) among multiple parties without directly sharing the raw data. However, several challenges exist when applying existing FL architectures to construct a data marketplace: (i) In existing FL architectures, Data Acquirers (DAs) cannot privately evaluate local models from Data Providers (DPs) prior to trading; (ii) Model aggregation protocols in existing FL designs struggle to exclude malicious DPs without "overfitting" to the DA's (possibly biased) root dataset; (iii) Prior FL designs lack a proper billing mechanism to enforce the DA to fairly allocate the reward according to contributions made by different DPs. To address above challenges, we propose martFL, the first federated learning architecture that is specifically designed to enable a secure utility-driven data marketplace. At a high level, martFL is powered by two innovative designs: (i) a quality-aware model aggregation protocol that achieves robust local model aggregation even when the DA's root dataset is biased; (ii) a verifiable data transaction protocol that enables the DA to prove, both succinctly and in zero-knowledge, that it has faithfully aggregates the local models submitted by different DPs according to the committed aggregation weights, based on which the DPs can unambiguously claim the corresponding reward. We implement a prototype of martFL and evaluate it extensively over various tasks. The results show that martFL can improve the model accuracy by up to 25% while saving up to 64% data acquisition cost.

cs.CR↗

arXiv2024

Revealing Decision Conservativeness Through Inverse Distributionally Robust Optimization

This paper introduces Inverse Distributionally Robust Optimization (I-DRO) as a method to infer the conservativeness level of a decision-maker, represented by the size of a Wasserstein metric-based ambiguity set, from the optimal decisions made using Forward Distributionally Robust Optimization (F-DRO). By leveraging the Karush-Kuhn-Tucker (KKT) conditions of the convex F-DRO model, we formulate I-DRO as a bi-linear program, which can be solved using off-the-shelf optimization solvers. Additionally, this formulation exhibits several advantageous properties. We demonstrate that I-DRO not only guarantees the existence and uniqueness of an optimal solution but also establishes the necessary and sufficient conditions for this optimal solution to accurately match the actual conservativeness level in F-DRO. Furthermore, we identify three extreme scenarios that may impact I-DRO effectiveness. Our case study applies F-DRO for power system scheduling under uncertainty and employs I-DRO to recover the conservativeness level of system operators. Numerical experiments based on an IEEE 5-bus system and a realistic NYISO 11-zone system demonstrate I-DRO performance in both normal and extreme scenarios.

math.OC↗

arXiv2024

General synthetic iterative scheme for rarefied gas mixture flows

The numerical simulation of rarefied gas mixtures with disparate mass and concentration is a huge research challenge. Based on our recent kinetic modelling for monatomic gas mixture flows, this problem is tackled by the general synthetic iterative scheme (GSIS), where the mesoscopic kinetic and macroscopic synthetic equations are alternately solved by the finite-volume discrete velocity method. Three important features of GSIS are highlighted. First, the synthetic equations are precisely derived from the kinetic equation, naturally reducing to the Navier-Stokes equations in the continuum flow regime; in other flow regimes, the kinetic equation provides high-order closure of the constitutive relations to capture the rarefaction effects. Second, these synthetic equations, which can be solved quickly, help to adjust the kinetic system to relax rapidly toward the steady state. Furthermore, in such a two-way coupling, the constraint on the spatial cell size is relieved. Third, the linear Fourier stability analysis demonstrates that the error decay rate in GSIS is smaller than 0.5 for various combinations of mass, concentration and viscosity ratios, such that the error can be reduced by three orders of magnitude after 10 iterations. The efficiency and accuracy of GSIS are demonstrated through several challenging cases covering a wide range of mass ratio, species concentration, and flow speed.

physics.comp-ph↗

arXiv2024

JNI Global References Are Still Vulnerable: Attacks and Defenses

System services and resources in Android are accessed through IPC based mechanisms. Previous research has demonstrated that they are vulnerable to the denial-of-service attack (DoS attack). For instance, the JNI global reference (JGR), which is widely used by system services, can be exhausted to cause the system reboot (hence the name JGRE attack). Even though the Android team tries to fix the problem by enforcing security checks, we find that it is still possible to construct a JGR exhaustion DoS attack in the latest Android system. In this paper, we propose a new JGR exhaustion DoS attack, which is effective in different Android versions, including the latest one (i.e., Android 10). Specifically, we developed JGREAnalyzer, a tool that can systematically detect JGR vulnerable services APIs via a call graph analysis and a forwarding reachability analysis. We applied this tool to different Android versions and found multiple vulnerabilities. In particular, among 148 system services in Android 10, 12 of them have 21 vulnerabilities. Among them, 9 can be successfully exploited without any permissions. We further analyze the root cause of the vulnerabilities and propose a new defense to mitigate the JGRE attack by restricting resource consumption via global reference counting.

cs.CR↗

arXiv2024

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness

By adopting a more flexible definition of unlearning and adjusting the model distribution to simulate training without the targeted data, approximate machine unlearning provides a less resource-demanding alternative to the more laborious exact unlearning methods. Yet, the unlearning completeness of target samples-even when the approximate algorithms are executed faithfully without external threats-remains largely unexamined, raising questions about those approximate algorithms' ability to fulfill their commitment of unlearning during the lifecycle. In this paper, we introduce the task of Lifecycle Unlearning Commitment Management (LUCM) for approximate unlearning and outline its primary challenges. We propose an efficient metric designed to assess the sample-level unlearning completeness. Our empirical results demonstrate its superiority over membership inference techniques in two key areas: the strong correlation of its measurements with unlearning completeness across various unlearning tasks, and its computational efficiency, making it suitable for real-time applications. Additionally, we show that this metric is able to serve as a tool for monitoring unlearning anomalies throughout the unlearning lifecycle, including both under-unlearning and over-unlearning. We apply this metric to evaluate the unlearning commitments of current approximate algorithms. Our analysis, conducted across multiple unlearning benchmarks, reveals that these algorithms inconsistently fulfill their unlearning commitments due to two main issues: 1) unlearning new data can significantly affect the unlearning utility of previously requested data, and 2) approximate algorithms fail to ensure equitable unlearning utility across different groups. These insights emphasize the crucial importance of LUCM throughout the unlearning lifecycle. We will soon open-source our newly developed benchmark.

cs.LG↗

arXiv2024

Efficient parallel solver for rarefied gas flow using GSIS

Recently, the general synthetic iterative scheme (GSIS) has been proposed to find the steady-state solution of the Boltzmann equation in the whole range of gas rarefaction, where its fast-converging and asymptotic-preserving properties lead to the significant reduction of iteration numbers and spatial cells in the near-continuum flow regime. However, the efficiency and accuracy of GSIS has only been demonstrated in two-dimensional problems with small numbers of spatial cells and discrete velocities. Here, a large-scale parallel computing strategy is designed to extend the GSIS to three-dimensional flow problems, including the supersonic flows which are usually difficult to solve by the discrete velocity method. Since the GSIS involves the calculation of the mesoscopic kinetic equation which is defined in six-dimensional phase-space, and the macroscopic high-temperature Navier-Stokes-Fourier equations in three-dimensional physical space, the proper partition of the spatial and velocity spaces, and the allocation of CPU cores to the mesoscopic and macroscopic solvers, are the keys to improving the overall computational efficiency. These factors are systematically tested to achieve optimal performance, up to 100 billion spatial and velocity grids. For hypersonic flows around the Apollo reentry capsule, the X38-like vehicle, and the space station, our parallel solver can obtain the converged solution within one hour.

physics.flu-dyn↗

arXiv2024

AceMap: Knowledge Discovery through Academic Graph

The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis of content within scientific publications. The representation of heterogeneous graphs and the effective measurement, analysis, and mining of such graphs pose significant challenges. To address these challenges, we present AceMap, an academic system designed for knowledge discovery through academic graph. We present advanced database construction techniques to build the comprehensive AceMap database with large-scale academic entities that contain rich visual, textual, and numerical information. AceMap also employs innovative visualization, quantification, and analysis methods to explore associations and logical relationships among academic entities. AceMap introduces large-scale academic network visualization techniques centered on nebular graphs, providing a comprehensive view of academic networks from multiple perspectives. In addition, AceMap proposes a unified metric based on structural entropy to quantitatively measure the knowledge content of different academic entities. Moreover, AceMap provides advanced analysis capabilities, including tracing the evolution of academic ideas through citation relationships and concept co-occurrence, and generating concise summaries informed by this evolutionary process. In addition, AceMap uses machine reading methods to generate potential new ideas at the intersection of different fields. Exploring the integration of large language models and knowledge graphs is a promising direction for future research in idea evolution. Please visit \url{https://www.acemap.info} for further exploration.

cs.DL↗

arXiv2024

PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users' fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users' fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in realworld scenarios show that Printlistener can significantly improve the attack potency of MasterPrint.

cs.CR↗

arXiv2024

A parameter-free clustering algorithm for missing datasets

Missing datasets, in which some objects have missing values in certain dimensions, are prevalent in the Real-world. Existing clustering algorithms for missing datasets first impute the missing values and then perform clustering. However, both the imputation and clustering processes require input parameters. Too many input parameters inevitably increase the difficulty of obtaining accurate clustering results. Although some studies have shown that decision graphs can replace the input parameters of clustering algorithms, current decision graphs require equivalent dimensions among objects and are therefore not suitable for missing datasets. To this end, we propose a Single-Dimensional Clustering algorithm, i.e., SDC. SDC, which removes the imputation process and adapts the decision graph to the missing datasets by splitting dimension and partition intersection fusion, can obtain valid clustering results on the missing datasets without input parameters. Experiments demonstrate that, across three evaluation metrics, SDC outperforms baseline algorithms by at least 13.7%(NMI), 23.8%(ARI), and 8.1%(Purity).

cs.LG↗