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CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

science China(information sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA school of Cyber Engineering Xidian University school of computer science and Technology Xidian University Department of computer and information science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and performance.MCAs in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

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Multi-instance partial-label learning: towards exploiting dual inexact supervision

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science China(information sciences) 2024年第3期67卷 48-61页

作者： Wei TANG Weijia ZHANG Min-Ling ZHANG school of computer science and Engineering Southeast University Key Laboratory of computer Network and information Integration (Southeast University) Ministry of Education school of information and Physical sciences The University of Newcastle

Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associated with not only multiple instances but also a candidate label set that contains one groundtruth label and some false positive labels. Specifically, at least one instance pertains to the ground-truth label while no instance belongs to the false positive labels. In this paper, we formalize such problems as multi-instance partial-label learning(MIPL). Existing multi-instance learning algorithms and partial-label learning algorithms are suboptimal for solving MIPL problems since the former fails to disambiguate a candidate label set, and the latter cannot handle a multi-instance bag. To address these issues, a tailored algorithm named MIPLGP, i.e., multi-instance partial-label learning with Gaussian processes, is proposed.MIPLGP first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an exclusive disambiguation strategy, and last induces a model based on the Gaussian processes. Experimental results on various datasets validate that MIPLGP is superior to well-established multi-instance learning and partial-label learning algorithms for solving MIPL problems.

关键词： machine learning multi-instance partial-label learning multi-instance learning partial-label learning Gaussian processes

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A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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science China(information sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU Institute of information science School of Computer and Information TechnologyBeijing Jiaotong University Beijing Key Laboratory of Advanced information science and Network Technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last decades.However, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1230 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： $\Phi$-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM

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YOLOv5ST:A Lightweight and Fast Scene Text Detector

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computers, Materials & Continua 2024年第4期79卷 909-926页

作者： Yiwei Liu Yingnan Zhao Yi Chen Zheng Hu Min Xia school of computer and science Nanjing University of Information Science and TechnologyNanjing210044China school of Automation Nanjing University of Information Science and TechnologyNanjing210044China

Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.

关键词： Scene text detection YOLOv5 lightweight object detection

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A Cover-Independent Deep Image Hiding Method Based on Domain Attention Mechanism

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computers, Materials & Continua 2024年第3期78卷 3001-3019页

作者： Nannan Wu Xianyi Chen James Msughter Adeke Junjie Zhao school of computer and Software Nanjing University of Information Science and TechnologyNanjing210044China school of Electronics and information Engineering Nanjing University of Information Science and TechnologyNanjing210044China

Recently,deep image-hiding techniques have attracted considerable attention in covert communication and high-capacity information hiding.However,these approaches have some limitations.For example,a cover image lacks self-adaptability,information leakage,or weak concealment.To address these issues,this study proposes a universal and adaptable image-hiding method.First,a domain attention mechanism is designed by combining the Atrous convolution,which makes better use of the relationship between the secret image domain and the cover image domain.Second,to improve perceived human similarity,perceptual loss is incorporated into the training process.The experimental results are promising,with the proposed method achieving an average pixel discrepancy(APD)of 1.83 and a peak signal-to-noise ratio(PSNR)value of 40.72 dB between the cover and stego images,indicative of its high-quality output.Furthermore,the structural similarity index measure(SSIM)reaches 0.985 while the learned perceptual image patch similarity(LPIPS)remarkably registers at 0.0001.Moreover,self-testing and cross-experiments demonstrate the model’s adaptability and generalization in unknown hidden spaces,making it suitable for diverse computer vision tasks.

关键词： Deep image hiding attention mechanism privacy protection data security visual quality

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Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

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Chinese Physics B 2024年第4期33卷 729-734页

作者：鲁婕妤谢小华卢亚平吴亚联李春来马铭磷 school of Automation and Electronic information Xiangtan UniversityXiangtan 411105China school of computer science school of Cyberspace science Xiangtan UniversityXiangtan 411105China

The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

关键词： small-world networks Rulkov neurons memristor synchronization

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Expression Complementary Disentanglement Network for Facial Expression Recognition

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Chinese Journal of Electronics 2024年第3期33卷 742-752页

作者： Shanmin WANG Hui SHUAI Lei ZHU Qingshan LIU College of computer science and Technology Nanjing University of Aeronautics and Astronautics College of computer and Software Nanjing University of Information Science and Technology school of information and Control Nanjing University of Information Science and Technology

Disentangling facial expressions from other disturbing facial attributes in face images is an essential topic for facial expression recognition.Previous methods only care about facial expression disentanglement(FED)itself,ignoring the negative effects of other facial attributes.Due to the annotations on limited facial attributes,it is difficult for existing FED solutions to disentangle all disturbance from the input face.To solve this issue,we propose an expression complementary disentanglement network(ECDNet).ECDNet proposes to finish the FED task during a face reconstruction process,so as to address all facial attributes during disentanglement.Different from traditional reconstruction models,ECDNet reconstructs face images by progressively generating and combining facial appearance and matching geometry.It designs the expression incentive(EIE) and expression inhibition(EIN) mechanisms,inducing the model to characterize the disentangled expression and complementary parts precisely.Facial geometry and appearance,generated in the reconstructed process,are dealt with to represent facial expressions and complementary parts,respectively.The combination of distinctive reconstruction model,EIE,and EIN mechanisms ensures the completeness and exactness of the FED task.Experimental results on RAF-DB,AffectNet,and CAER-S datasets have proven the effectiveness and superiority of ECDNet.

关键词： Geometry Image recognition Annotations Face recognition Task analysis Image reconstruction Facial features

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One-step hydrothermal synthesis of Cit-NaYbF_(4):Er^(3+)nanocrystals with enhanced red upconversion emission for in vivo fluorescence molecular tomography

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Journal of Rare Earths 2024年第1期42卷 36-45,I0002页

作者： Xiaoli Luo Qichen Chen Hongbo Guo Heng Zhang Xiaowei He Wu Zhao school of Mathematics and computer science Ningxia Normal UniversityGuyuan756099China school of information science and Technology Northwest UniversityXi'an710127China

Fluorescence imaging techniques represent essential tools in in vitro,preclinical,and clinical studies.In this study,an improved one-step hydrothermal method to synthesize citric acid(CA)modifiedα-NaYbF_(4):2%Er^(3+)nanocrystals was proposed.The introduction of various doping ions into NaYbF_(4):2%Er^(3+)and the different valence states of the same ions affect both the crystal size and upconversion luminesce nce.There fore,we investigated the upconversion luminesce nce enha ncement of NaYbF_(4):2%Er^(3+)by ion doping and find that the upconversion luminescence intensity of the upconversion nanoparticles(UCNPs)co-doped with 5 mol%Fe^(2+)ions shows the greatest enhancement,especially for red emission at654 nm.Furthermore,HeLa cells incubated with UCNPs allow for imaging with strong red upconversion emission detectio n.Confocal laser scanning microscope(CLSM)fluorescent images of HeLa cells indicate that NaYbF_(4):2%Er/5%Fe^(2+)leads to a clear outline and improves visualization of the cell morphology.In addition,the CA coated NaYbF_(4):2%Er^(3+)/5%Fe^(2+)nanoparticles and NaYbF_(4):2%Er^(3+)/5%Fe^(2+)show low cytotoxicity in HeLa cells.Organ imaging reveals the efficiency of these UCNPs to analyze the lungs,liver,and spleen.Together,these results indicate that the Cit-NaYbF_(4):2%Er^(3+)/5%Fe^(2+)UCNPs are efficient nanoprobes for fluorescence molecular to mography.

关键词： Hydrothermal method Fe^(2+)ion doping Tuning size Nanoprobes Fluorescence imaging Rare earths

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Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

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Chinese Physics B 2024年第2期33卷 537-544页

作者：晏询李志军李春来 school of Automation and Electronic information Xiangtan UniversityXiangtan 411105China school of computer science&school of Cyberspace science Xiangtan UniversityXiangtan 411105China

Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

关键词： heterogeneous neuron network discrete memristor coexisting attractors synchronization noise

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Modifying Lyapunov exponent of chaotic map by self-cascading

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science China Technological sciences 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of Electronic and information Engineering Nanjing University of Information Science & Technology school of Artificial Intelligence Nanjing University of Information Science & Technology school of computer science and Technology Harbin Institute of Technology Shenzhen Graduate School

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos maps.Additionally, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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