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Lossless and Universal 3D Object Encryption with Differentiated Visual Effects upon Decryption: A Novel Paradigm

IEEE Transactions on Dependable and Secure computing 2025年

作者： Zhao, Ruoyu Zhang, Yushu Mou, Jun Puech, William Weng, Jian School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang330013 China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang330013 China School of Information Science and Engineering Dalian Polytechnic University Dalian116000 China Centre National de la Recherche Scientifique Université de Montpellier Montpellier34095 France College of Information Science and Technology Jinan University Guangzhou510632 China

A recently proposed 3D object encryption scheme enables hierarchical decryption, allowing a single encryption to display varied visual effects upon decryption. This has potential applications in complex scenarios where 3D objects require access at different security levels. However, it has two main issues: lossless decryption is not possible due to precision loss from the IEEE 754 standard, and it is customized for a specific AES algorithm, limiting support for others. Motivated by this, a novel paradigm for 3D object encryption with differentiated visual effects upon decryption is proposed. In this paradigm, the precision loss is preserved within the 3D encrypted object, allowing the 3D object after fully decrypting to be identical to the original, and thus it is lossless. Meanwhile, the data to be encrypted is reduced to three blocks, which are generalized bitstreams. Bitstream encryption algorithms that do not produce ciphertext expansion can be applied, and it operates independently of the other components in the paradigm, making it universal. Finally, a prototype of this paradigm is constructed, using the same encryption algorithm as in the previous scheme for comparative experiments. Two 3D object datasets are used to conduct the experiments and results demonstrate that it achieves lossless decryption and has a time advantage, while also undergoing security analysis and verification against smoothing attacks to confirm the security. © 2025 IEEE. All rights reserved,

关键词： Differential privacy

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Study of Resistive Properties and Neural Response of Zro2/Tio2 Heterojunction Nanowire Array (Nwa) Rram

SSRN

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SSRN 2024年

作者： Sun, Tangyou Qin, Zubin Yu, Fantao Gao, Shuai Wangyang, Peihua Tang, Xiaosheng Li, Haiou Zhang, Fabi Xu, Zhimou Cai, Ping Jiang, Chunsheng Xue, Xiaogang Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China College of Optoelectronic Engineering Chongqing University Chongqing400044 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Guangxi Key Laboratory of Information Materials Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guangxi Normal University Guilin541004 China School of Optoelectronic Engineering Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin541004 China

Nowadays, researcher has an eye on enhancing the performance of resistive random-access memory (RRAM) and exploring new demands and applications, particularly in the field of artificial intelligence and neural computing. It is crucially important for accurately replicating the observed plasticity within synapses to achieve superior RRAM performance in low-dimensional nanomaterials. In this work, we focus on addressing certainly unsatisfactory electrical property of titanium oxide nanowire array (NWA)-based RRAM, for example short retention time (~ 103 s). Theoretically and experimentally, a novel and solution-based RRAM structure, namely ZrO2/TiO2(NWA), has been conducted. With the help of ZrO2 insertion layer, the RRAM devices shows impressive resistive switching characteristics, including endurance cycling (> 105 cycles), and a long retention time (~ 9×104 s). These findings hold significant implications for digital computing systems. Moreover, successful implementation of the proposed device enables the emulation of fundamental synaptic biological features such as long-term potentiation (LTP), long-term depression (LTD), non-linear transmission properties, and learning experiences. This research provides evidence of the immense potential of the ZrO2/TiO2(NWA) RRAM device in non-volatile memory (NVM) and biomimetic neuromorphic systems. © 2024, The Authors. All rights reserved.

关键词： Heterojunctions

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Improved Uniform Linear Array Fitting Scheme with Increased Lower Bound on Uniform Degrees of Freedom for DOA Estimation

TechRxiv

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TechRxiv 2022年

作者： Shi, Wanlu Li, Yingsong The College of Information and Communication Engineering Harbin Engineering University Harbin150001 China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

Recently, uniform linear array (ULA) fitting (UF) principle is proposed for sparse array (SA) design using pseudo-polynomial equations. Typically, it is verified that the designed SAs via UF enjoy a lower bound on uniform degrees of freedom (uDOF), which is approximately 0.5N2 with N sensors. Herein, an improved UF (IUF) scheme is proposed to significantly increase the lower bound on uDOF, which is realized by introducing two sub-ULAs, with name of adjoint transfer arrays (ATAs), on both sides of a transfer sub-ULA. The ATAs together with the transfer sub-ULA serve as a new layer to construct an adjoint transfer layer (ATL) that has improved aperture in comparison with traditional transfer layer to improve the lower bound on uDOF, which is derived in detail. Furthermore, two novel SAs are developed based on the ATL to verify the effectiveness of the proposed IUF scheme to increase the uDOF. Numerical simulations verify the superiority of devised SAs for direction-of-arrival (DOA) estimations. © 2022, CC BY.

关键词： Polynomials

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Memristor-based hyper-chaotic circuit for image encryption

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Chinese Physics B 2020年第11期29卷 259-270页

作者： Jiao-Jiao Chen Deng-Wei Yan Shu-Kai Duan Li-Dan Wang College of Electronic and Information Engineering Southwest UniversityChongqing 400715China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest UniversityChongqing 400715China Intelligent Transmission and Control Technology Joint Engineering Laboratory Chongqing 400715China Brain-inspired Computing and Intelligent Control Chongqing Key Laboratory Chongqing 400715China Chongqing Collaborative Innovation Center for Brain Science Chongqing 400715China School of Artificial Intelligence Southwest UniversityChongqing 400715China

The memristor is a kind of non-linear element with memory function,which can be applied to chaotic systems to increase signal randomness and *** this paper,a new four-dimensional hyper-chaotic system is designed based on a flux controlled memristor model,which can generate complex chaotic *** basic dynamic theory analysis and numerical simulations of the system,such as the stability of equilibrium points,the Lyapunov exponents and dimension,Poincare maps,the power spectrum,and the waveform graph prove that it has rich dynamic ***,the circuit implementation of this system is *** consistency of simulation program with integrated circuit emphasis(SPICE)simulation and numerical analysis proves the ability to generate ***,a new image encryption scheme is designed by using the memristor-based hyper-chaotic system proposed in this *** scheme involves a total of two *** using different security analysis factors,the proposed algorithm is compared with other image encryption schemes,including correlation analysis,information entropy,*** results show that the proposed image encryption scheme has a large key space and presents a better encryption effect.

关键词： memristor SPICE simulation hyper-chaotic system image encryption

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Tiny Object Tracking: A Large-scale Dataset and A Baseline

arXiv

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arXiv 2022年

作者： Zhu, Yabin Li, Chenglong Liu, Yao Wang, Xiao Tang, Jin Luo, Bin Huang, Zhixiang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Anhui Higher Education Institutes Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China Peng Cheng Laboratory Shenzhen China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Anhui Higher Education Institutes Anhui University Hefei230601 China

Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection and segmentation. To promote the research and development of tiny object tracking, we create a large-scale video dataset, which contains 434 sequences with a total of more than 217K frames. Each frame is carefully annotated with a high-quality bounding box. In data creation, we take 12 challenge attributes into account to cover a broad range of viewpoints and scene complexities, and annotate these attributes for facilitating the attribute-based performance analysis. To provide a strong baseline in tiny object tracking, we propose a novel Multilevel Knowledge Distillation Network (MKDNet), which pursues three-level knowledge distillations in a unified framework to effectively enhance the feature representation, discrimination and localization abilities in tracking tiny objects. Extensive experiments are performed on the proposed dataset, and the results prove the superiority and effectiveness of MKDNet compared with state-of-the-art methods. The dataset, the algorithm code, and the evaluation code are available at https://***/mmic-lcl/Datasets-andbenchmark-code. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Compact Broadband Rectifier With Wide Input Power Range Based on Adaptive Input Power Distribution

TechRxiv

引用

TechRxiv 2022年

作者： Liu, Xin Li, Yingsong Zhang, Dawei College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

This paper presents a novel compact rectifier array with both broadband and wide input power range characteristics. The rectifier array consists of two rectifier units operating at low-power and high-power levels, respectively, and an adaptive input power distribution network (AIPDN) enabling automatic distribution of radio frequency (RF) energy at different power levels. In addition, a matching inductance is introduced to counteract the capacitance of the diode. As a result, the high RF-DC power conversion efficiency (PCE) is achieved in both broadband and wide input power range. For demonstration, a prototype of the presented rectifier array is fabricated and measured. The measured results are in good accordance with the simulated results. With an input power range of 8 – 25 dBm and a bandwidth of 1.82 – 2.75 GHz (fractional bandwidth of 40.7%), the PCE is greater than 50%. At 2.4 GHz, the input power ranges from -5.5 to 27.5 dBm with PCE greater than 50%. Besides, a peak PCE of 72.1% is obtained at an input power of 25 dBm. This work can lay the foundation for the development of a new RF energy harvest systems with wide frequency band and dynamic input power. © 2022, CC BY.

关键词： Horn antennas

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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Transient Convergence Analysis of Least Lncosh Algorithm

TechRxiv

引用

TechRxiv 2022年

作者： Sun, Zeyang Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei China

Lncosh function has been used for constructing a cost function for devising an adaptive filter algorithm to provide a desired performance under non-Gaussian noise, naming as least Lncosh algorithm (LLA). However, its transient behavior analysis has not been analyzed and investigated. As an important characteristic for the LLA, the transient behavior affects its performance in real-world applications. Herein, we present the theoretical model for evaluating the mean weight behavior and the transient excess mean square error (EMSE) behavior of the LLA. In addition, the proposed models can also be used to predict the transient behavior of the adaptive filtering algorithms with error nonlinearities under non-Gaussian noises. The experiment results have verified the validity of the presented models. © 2022, CC BY.

关键词： Adaptive filters

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Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection

arXiv

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arXiv 2023年

作者： Zhang, Chen Li, Guorong Qi, Yuankai Ye, Hanhua Qing, Laiyun Yang, Ming-Hsuan Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Australian Institute for Machine Learning The University of Adelaide Australia University of California Merced United States Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China

The goal of weakly supervised video anomaly detection is to learn a detection model using only video-level labeled data. However, prior studies typically divide videos into fixed-length segments without considering the complexity or duration of anomalies. Moreover, these studies usually just detect the most abnormal segments, potentially overlooking the completeness of anomalies. To address these limitations, we propose a Dynamic Erasing Network (DE-Net) for weakly supervised video anomaly detection, which learns multi-scale temporal features. Specifically, to handle duration variations of abnormal events, we first propose a multi-scale temporal modeling module, capable of extracting features from segments of varying lengths and capturing both local and global visual information across different temporal scales. Then, we design a dynamic erasing strategy, which dynamically assesses the completeness of the detected anomalies and erases prominent abnormal segments in order to encourage the model to discover gentle abnormal segments in a video. The proposed method obtains favorable performance compared to several state-of-the-art approaches on three datasets: XD-Violence, TAD, and UCF-Crime. Code will be made available at https://***/ArielZc/DE-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest

Using Rough Sets to Improve the High-dimensional Data Anomal...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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