arXiv subjects

Qiang Zeng

Publications and source records attributed to Qiang Zeng.

At least 37 records · Page 2Linked to original sources

arXiv2024

Turning Noises to Fingerprint-Free "Credentials": Secure and Usable Drone Authentication

Drones have been widely used in various services, such as delivery and surveillance. Authentication forms the foundation of the security of these services. However, drones are expensive and may carry important payloads. To avoid being captured by attackers, drones should keep a safe distance from the verifier before authentication succeeds. This makes authentication methods that only work in very close proximity not applicable. Our work leverages drone noises for authentication. While using sounds for authentication is highly usable, how to handle various attacks that manipulate sounds is an \emph{unresolved challenge}. It is also unclear how to ensure robustness under various environmental sounds. Being the first in the literature, we address the two major challenges by exploiting unique characteristics of drone noises. We thereby build an authentication system that does \emph{not} rely on any drone sound fingerprints, keeps resilient to attacks, and is robust under environmental sounds. An extensive evaluation demonstrates its security and usability.

cs.CR↗

arXiv2024

Progress in artificial intelligence applications based on the combination of self-driven sensors and deep learning

In the era of Internet of Things, how to develop a smart sensor system with sustainable power supply, easy deployment and flexible use has become a difficult problem to be solved. The traditional power supply has problems such as frequent replacement or charging when in use, which limits the development of wearable devices. The contact-to-separate friction nanogenerator (TENG) was prepared by using polychotomy thy lene (PTFE) and aluminum (AI) foils. Human motion energy was collected by human body arrangement, and human motion posture was monitored according to the changes of output electrical signals. In 2012, Academician Wang Zhong lin and his team invented the triboelectric nanogenerator (TENG), which uses Maxwell displacement current as a driving force to directly convert mechanical stimuli into electrical signals, so it can be used as a self-driven sensor. Teng-based sensors have the advantages of simple structure and high instantaneous power density, which provides an important means for building intelligent sensor systems. At the same time, machine learning, as a technology with low cost, short development cycle, strong data processing ability and prediction ability, has a significant effect on the processing of a large number of electrical signals generated by TENG, and the combination with TENG sensors will promote the rapid development of intelligent sensor networks in the future. Therefore, this paper is based on the intelligent sound monitoring and recognition system of TENG, which has good sound recognition capability, and aims to evaluate the feasibility of the sound perception module architecture in ubiquitous sensor networks.

eess.SP↗

arXiv2024

On the expected number of critical points of locally isotropic Gaussian random fields

We consider locally isotropic Gaussian random fields on the $N$-dimensional Euclidean space for fixed $N$. Using the so called Gaussian Orthogonally Invariant matrices first studied by Mallows in 1961 which include the celebrated Gaussian Orthogonal Ensemble (GOE), we establish the Kac--Rice representation of expected number of critical points of non-isotropic Gaussian fields, complementing the isotropic case obtained by Cheng and Schwartzman in 2018. In the limit $N=\infty$, we show that such a representation can be always given by GOE matrices, as conjectured by Auffinger and Zeng in 2020.

math.PR↗

arXiv2023

UMMAFormer: A Universal Multimodal-adaptive Transformer Framework for Temporal Forgery Localization

The emergence of artificial intelligence-generated content (AIGC) has raised concerns about the authenticity of multimedia content in various fields. However, existing research for forgery content detection has focused mainly on binary classification tasks of complete videos, which has limited applicability in industrial settings. To address this gap, we propose UMMAFormer, a novel universal transformer framework for temporal forgery localization (TFL) that predicts forgery segments with multimodal adaptation. Our approach introduces a Temporal Feature Abnormal Attention (TFAA) module based on temporal feature reconstruction to enhance the detection of temporal differences. We also design a Parallel Cross-Attention Feature Pyramid Network (PCA-FPN) to optimize the Feature Pyramid Network (FPN) for subtle feature enhancement. To evaluate the proposed method, we contribute a novel Temporal Video Inpainting Localization (TVIL) dataset specifically tailored for video inpainting scenes. Our experiments show that our approach achieves state-of-the-art performance on benchmark datasets, including Lav-DF, TVIL, and Psynd, significantly outperforming previous methods. The code and data are available at https://github.com/ymhzyj/UMMAFormer/.

cs.MM↗

arXiv2023

Controlled entanglement source for quantum cryptography

Quantum entanglement has become an essential resource in quantum information processing. Existing works employ entangled quantum states to perform various tasks, while little attention is paid to the control of the resource. In this work, we propose a simple protocol to upgrade an entanglement source with access control through phase randomization at the optical pump. The enhanced source can effectively control all users in utilizing the entanglement resource to implement quantum cryptography. In addition, we show this control can act as a practical countermeasure against memory attack on device-independent quantum key distribution at a negligible cost. To demonstrate the feasibility of our protocol, we implement an experimental setup using just off-the-shelf components and characterize its performance accordingly.

quant-ph↗

arXiv2022

One-way Einstein-Podolsky-Rosen steering beyond qubits

Quantum steering has been exploited as an important resource in modern quantum information processing. Owing to its directional nature, some quantum states that are asymmetric under the exchange of parties have been found to manifest steering only in specific direction, thus called one-way steering. Existing works focused on one-way steering in systems of qubits. Here we propose a family of two-party states that are one-way steerable in systems of $d$-dimension. In particular, we validate the one-way steerability of the states for $d=3$, and demonstrate how one-way steering parameter space manifests in two-qutrit system. A general numerical approach for characterizing higher-dimensional one-way steering is provided. Moreover, we develop a method to characterize one-way steering with the experimental loss taken into account, with which the tradeoff relation between losses and measurement settings in steering test in higher-dimensional system is investigated. Our loss-counted model works for finite-dimensional system with finite measurement settings.

quant-ph↗

arXiv2022

Complexity of Gaussian random fields with isotropic increments

We study the energy landscape of a model of a single particle on a random potential, that is, we investigate the topology of level sets of smooth random fields on $\mathbb R^{N}$ of the form $X_N(x) +\frac\mu2 \|x\|^2,$ where $X_{N}$ is a Gaussian process with isotropic increments. We derive asymptotic formulas for the mean number of critical points with critical values in an open set as the dimension $N$ goes to infinity. In a companion paper, we provide the same analysis for the number of critical points with a given index.

math.PR↗

arXiv2022

Complexity of Gaussian random fields with isotropic increments: critical points with given indices

We study the landscape complexity of the Hamiltonian $X_N(x) +\frac\mu2 \|x\|^2,$ where $X_{N}$ is a smooth Gaussian process with isotropic increments on $\mathbb R^{N}$. This model describes a single particle on a random potential in statistical physics. We derive asymptotic formulas for the mean number of critical points of index $k$ with critical values in an open set as the dimension $N$ goes to infinity. In a companion paper, we provide the same analysis without the index constraint.

math.PR↗

arXiv2022

Reliable experimental certification of one-way Einstein-Podolsky-Rosen steering

Quantum steering is a recently-defined form of quantum correlation which lies at the heart of quantum mechanics. In difference from other types of quantum correlations, quantum steering is inherently asymmetric, which implies that it could manifest in one direction but not in the opposite direction. This rather peculiar phenomenon, known as one-way steering, have been demonstrated in several experiments, but subtlety remains. In fact all experiments were shown to be ambiguous until a very recent conclusive one, which however made a crucial use of a high dimensional embedding to get around assumptions. This leaves the question open whether the one-way steering phenomenon can be reliably demonstrated in the genuine two-qubit system. Here, we report such an experimental demonstration of one-way steering for a family of two-qubit states. Our experimental setup and results thus resolve the subtlety caused by fidelity assumption in previous experiments without the need of higher dimensional embedding. Moreover, our work provides a universal method to characterize one-way steering phenomenon for generic two-qubit states.

quant-ph↗

arXiv2021

The SK model is infinite step replica symmetry breaking at zero temperature

We prove that the Parisi measure of the mixed p-spin model at zero temperature has infinitely many points in its support. This establishes Parisi's prediction that the functional order parameter of the Sherrington-Kirkpatrick model is not a step function at zero temperature. As a consequence, we show that the number of levels of broken replica symmetry in the Parisi formula of the free energy diverges as the temperature goes to zero.

math.PR↗

arXiv2021

PFirewall: Semantics-Aware Customizable Data Flow Control for Smart Home Privacy Protection

Internet of Things (IoT) platforms enable users to deploy home automation applications. Meanwhile, privacy issues arise as large amounts of sensitive device data flow out to IoT platforms. Most of the data flowing out to a platform actually do not trigger automation actions, while homeowners currently have no control once devices are bound to the platform. We present PFirewall, a customizable data-flow control system to enhance the privacy of IoT platform users. PFirewall automatically generates data-minimization policies, which only disclose minimum amount of data to fulfill automation. In addition, PFirewall provides interfaces for homeowners to customize individual privacy preferences by defining user-specified policies. To enforce these policies, PFirewall transparently intervenes and mediates the communication between IoT devices and the platform, without modifying the platform, IoT devices, or hub. Evaluation results on four real-world testbeds show that PFirewall reduces IoT data sent to the platform by 97% without impairing home automation, and effectively mitigates user-activity inference/tracking attacks and other privacy risks.

cs.CR↗

arXiv2021

Cross-App Interference Threats in Smart Homes: Categorization, Detection and Handling

A number of Internet of Things (IoTs) platforms have emerged to enable various IoT apps developed by third-party developers to automate smart homes. Prior research mostly concerns the overprivilege problem in the permission model. Our work, however, reveals that even IoT apps that follow the principle of least privilege, when they interplay, can cause unique types of threats, named Cross-App Interference (CAI) threats. We describe and categorize the new threats, showing that unexpected automation, security and privacy issues may be caused by such threats, which cannot be handled by existing IoT security mechanisms. To address this problem, we present HOMEGUARD, a system for appified IoT platforms to detect and cope with CAI threats. A symbolic executor module is built to precisely extract the automation semantics from IoT apps. The semantics of different IoT apps are then considered collectively to evaluate their interplay and discover CAI threats systematically. A user interface is presented to users during IoT app installation, interpreting the discovered threats to help them make decisions. We evaluate HOMEGUARD via a proof-of-concept implementation on Samsung SmartThings and discover many threat instances among apps in the SmartThings public repository. The evaluation shows that it is precise, effective and efficient.

cs.CR↗

arXiv2020

Exploiting the Sensitivity of $L_2$ Adversarial Examples to Erase-and-Restore

By adding carefully crafted perturbations to input images, adversarial examples (AEs) can be generated to mislead neural-network-based image classifiers. $L_2$ adversarial perturbations by Carlini and Wagner (CW) are among the most effective but difficult-to-detect attacks. While many countermeasures against AEs have been proposed, detection of adaptive CW-$L_2$ AEs is still an open question. We find that, by randomly erasing some pixels in an $L_2$ AE and then restoring it with an inpainting technique, the AE, before and after the steps, tends to have different classification results, while a benign sample does not show this symptom. We thus propose a novel AE detection technique, Erase-and-Restore (E&R), that exploits the intriguing sensitivity of $L_2$ attacks. Experiments conducted on two popular image datasets, CIFAR-10 and ImageNet, show that the proposed technique is able to detect over 98% of $L_2$ AEs and has a very low false positive rate on benign images. The detection technique exhibits high transferability: a detection system trained using CW-$L_2$ AEs can accurately detect AEs generated using another $L_2$ attack method. More importantly, our approach demonstrates strong resilience to adaptive $L_2$ attacks, filling a critical gap in AE detection. Finally, we interpret the detection technique through both visualization and quantification.

cs.CV↗

arXiv2020

POKs Based Secure and Energy-Efficient Access Control for Implantable Medical Devices

Implantable medical devices (IMDs), such as pacemakers, implanted cardiac defibrillators, and neurostimulators are medical devices implanted into patients' bodies for monitoring physiological signals and performing medical treatments. Many IMDs have built-in wireless communication modules to facilitate data collecting and device reprogramming by external programmers. The wireless communication brings significant conveniences for advanced applications such as real-time and remote monitoring but also introduces the risk of unauthorized wireless access. The absence of effective access control mechanisms exposes patients' life to cyber attacks. In this paper, we present a lightweight and universally applicable access control system for IMDs. By leveraging Physically Obfuscated Keys (POKs) as the hardware root of trust, provable security is achieved based on standard cryptographic primitives while attaining high energy efficiency. In addition, barrier-free IMD access under emergent situations is realized by utilizing the patient's biometrical information. We evaluate our proposed scheme through extensive security analysis and a prototype implementation, which demonstrates our work's superiority on security and energy efficiency.

cs.CR↗

arXiv2019

A Multiversion Programming Inspired Approach to Detecting Audio Adversarial Examples

Adversarial examples (AEs) are crafted by adding human-imperceptible perturbations to inputs such that a machine-learning based classifier incorrectly labels them. They have become a severe threat to the trustworthiness of machine learning. While AEs in the image domain have been well studied, audio AEs are less investigated. Recently, multiple techniques are proposed to generate audio AEs, which makes countermeasures against them an urgent task. Our experiments show that, given an AE, the transcription results by different Automatic Speech Recognition (ASR) systems differ significantly, as they use different architectures, parameters, and training datasets. Inspired by Multiversion Programming, we propose a novel audio AE detection approach, which utilizes multiple off-the-shelf ASR systems to determine whether an audio input is an AE. The evaluation shows that the detection achieves accuracies over 98.6%.

cs.SD↗

arXiv2019

Towards Efficient Integration of Blockchain for IoT Security: The Case Study of IoT Remote Access

The booming Internet of Things (IoT) market has drawn tremendous interest from cyber attackers. The centralized cloud-based IoT service architecture has serious limitations in terms of security, availability, and scalability, and is subject to single points of failure (SPOF). Recently, accommodating IoT services on blockchains has become a trend for better security, privacy, and reliability. However, blockchain's shortcomings of high cost, low throughput, and long latency make it unsuitable for IoT applications. In this paper, we take a retrospection of existing blockchain-based IoT solutions and propose a framework for efficient blockchain and IoT integration. Following the framework, we design a novel blockchain-assisted decentralized IoT remote accessing system, RS-IoT, which has the advantage of defending IoT devices against zero-day attacks without relying on any trusted third-party. By introducing incentives and penalties enforced by smart contracts, our work enables "an economic approach" to thwarting the majority of attackers who aim to achieve monetary gains. Our work presents an example of how blockchain can be used to ensure the fairness of service trading in a decentralized environment and punish misbehaviors objectively. We show the security of RS-IoT via detailed security analyses. Finally, we demonstrate its scalability, efficiency, and usability through a proof-of-concept implementation on the Ethereum testnet blockchain.

cs.CR↗

arXiv2019

PFirewall: Semantics-Aware Customizable Data Flow Control for Home Automation Systems

Emerging Internet of Thing (IoT) platforms provide a convenient solution for integrating heterogeneous IoT devices and deploying home automation applications. However, serious privacy threats arise as device data now flow out to the IoT platforms, which may be subject to various attacks. We observe two privacy-unfriendly practices in emerging home automation systems: first, the majority of data flowed to the platform are superfluous in the sense that they do not trigger any home automation; second, home owners currently have nearly zero control over their data. We present PFirewall, a customizable data-flow control system to enhance user privacy. PFirewall analyzes the automation apps to extract their semantics, which are automatically transformed into data-minimization policies; these policies only send minimized data flows to the platform for app execution, such that the ability of attackers to infer user privacy is significantly impaired. In addition, PFirewall provides capabilities and interfaces for users to define and enforce customizable policies based on individual privacy preferences. PFirewall adopts an elegant man-in-the-middle design, transparently executing data minimization and user-defined policies to process raw data flows and mediating the processed data between IoT devices and the platform (via the hub), without requiring modifications of the platform or IoT devices. We implement PFirewall to work with two popular platforms: SmartThings and openHAB, and set up two real-world testbeds to evaluate its performance. The evaluation results show that PFirewall is very effective: it reduces IoT data sent to the platform by 97% and enforces user defined policies successfully.

cs.CR↗

arXiv2019

Exploiting the Inherent Limitation of L0 Adversarial Examples

Despite the great achievements made by neural networks on tasks such as image classification, they are brittle and vulnerable to adversarial example (AE) attacks, which are crafted by adding human-imperceptible perturbations to inputs in order that a neural-network-based classifier incorrectly labels them. In particular, L0 AEs are a category of widely discussed threats where adversaries are restricted in the number of pixels that they can corrupt. However, our observation is that, while L0 attacks modify as few pixels as possible, they tend to cause large-amplitude perturbations to the modified pixels. We consider this as an inherent limitation of L0 AEs, and thwart such attacks by both detecting and rectifying them. The main novelty of the proposed detector is that we convert the AE detection problem into a comparison problem by exploiting the inherent limitation of L0 attacks. More concretely, given an image I, it is pre-processed to obtain another image I' . A Siamese network, which is known to be effective in comparison, takes I and I' as the input pair to determine whether I is an AE. A trained Siamese network automatically and precisely captures the discrepancies between I and I' to detect L0 perturbations. In addition, we show that the pre-processing technique, inpainting, used for detection can also work as an effective defense, which has a high probability of removing the adversarial influence of L0 perturbations. Thus, our system, called AEPECKER, demonstrates not only high AE detection accuracies, but also a notable capability to correct the classification results.

cs.CR↗