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Detecting Double Mixed Compressed Images Based on Quaternion Convolutional Neural Network

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Chinese Journal of Electronics 2024年第3期33卷 657-671页

作者： Hao WANG Jinwei WANG Xuelong HU Bingtao HU Qilin YIN xiangyang luo Bin MA Jinsheng SUN Department of Automation Nanjing University of Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Department of Computer and Software Nanjing University of Information Science and Technology School of Management Nanjing University of Posts and Telecommunications Department of Computer Science and Engineering Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University State Key Laboratory of Mathematical Engineering and Advanced Computing Qilu University of Technology

Detection of color images that have undergone double compression is a critical aspect of digital image forensics.Despite the existence of various methods capable of detecting double Joint Photographic Experts Group(JPEG) compression,they are unable to address the issue of mixed double compression resulting from the use of different compression standards.In particular,the implementation of Joint Photographic Experts Group 2000(JPEG2000)as the secondary compression standard can result in a decline or complete loss of performance in existing methods.To tackle this challenge of JPEG+JPEG2000 compression,a detection method based on quaternion convolutional neural networks(QCNN) is proposed.The QCNN processes the data as a quaternion,transforming the components of a traditional convolutional neural network(CNN) into a quaternion representation.The relationships between the color channels of the image are preserved,and the utilization of color information is optimized.Additionally,the method includes a feature conversion module that converts the extracted features into quaternion statistical features,thereby amplifying the evidence of double compression.Experimental results indicate that the proposed QCNN-based method improves,on average,by 27% compared to existing methods in the detection of JPEG+JPEG2000 compression.

关键词： Image coding Image color analysis Quaternions Forensics Transform coding Color Feature extraction

Lightweight Steganography Detection Method Based on Multiple Residual Structures and Transformer

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Chinese Journal of Electronics 2024年第4期33卷 965-978页

作者： Hao LI Yi ZHANG Jinwei WANG Weiming ZHANG xiangyang luo Key Laboratory of Cyberspace Situation Awareness of Henan Province Nanjing University of Information Science & Technology University of Science and Technology of China

Existing deep learning-based steganography detection methods utilize convolution to automatically capture and learn steganographic features, yielding higher detection efficiency compared to manually designed steganography detection methods. Detection methods based on convolutional neural network frameworks can extract global features by increasing the network's depth and width. These frameworks are not highly sensitive to global features and can lead to significant resource consumption. This manuscript proposes a lightweight steganography detection method based on multiple residual structures and Transformer(Res Former). A multi-residuals block based on channel rearrangement is designed in the preprocessing layer. Multiple residuals are used to enrich the residual features and channel shuffle is used to enhance the feature representation capability. A lightweight convolutional and Transformer feature extraction backbone is constructed, which reduces the computational and parameter complexity of the network by employing depth-wise separable convolutions. This backbone integrates local and global image features through the fusion of convolutional layers and Transformer, enhancing the network's ability to learn global features and effectively enriching feature diversity. An effective weighted loss function is introduced for learning both local and global features, Bias Loss loss function is used to give full play to the role of feature diversity in classification, and cross-entropy loss function and contrast loss function are organically combined to enhance the expression ability of features. Based on Boss Base-1.01, BOWS2 and ALASKA#2, extensive experiments are conducted on the stego images generated by spatial and JPEG domain adaptive steganographic algorithms, employing both classical and state-of-theart steganalysis techniques. The experimental results demonstrate that compared to the SRM, SRNet, Sia Steg Net,CSANet, LWENet, and Sia IRNet methods, the proposed Res Former method achieves the highest reduction in

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Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths

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Digital Communications and Networks 2022年第4期8卷 455-465页

作者： Jialin Li xiangyang luo Yi Zhang Pei Zhang Chunfang Yang Fenlin Liu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000China

Privacy protection is the key to maintaining the Internet of Things(IoT)communication strategy.Steganography is an important way to achieve covert communication that protects user data privacy.Steganalysis technology is the key to checking steganography security,and its ultimate goal is to extract embedded messages.Existing methods cannot extract under known cover images.To this end,this paper proposes a method of extracting embedded messages under known cover images.First,the syndrome-trellis encoding process is analyzed.Second,a decoding path in the syndrome trellis is obtained by using the stego sequence and a certain parity-check matrix,while the embedding process is simulated using the cover sequence and parity-check matrix.Since the decoding path obtained by the stego sequence and the correct parity-check matrix is optimal and has the least distortion,comparing the path consistency can quickly filter the coding parameters to determine the correct matrices,and embedded messages can be extracted correctly.The proposed method does not need to embed all possible messages for the second time,improving coding parameter recognition significantly.The experimental results show that the proposed method can identify syndrome-trellis coding parameters in stego images embedded by adaptive steganography quickly to realize embedded message extraction.

关键词： Information security Image steganography STC coding Parameter recognition

Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

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Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu xiangyang luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method selection.To address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short windows.Based on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each window.Then,the PoI similarities between long and short windows are calculated for training and prediction.Finally,series of experiments is conducted based on real Internet forum datasets.The results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed framework.When the length of long window is small,traditional methods perform better,and SVR is the best.On the contrary,the deep learning methods show superiority,and LSTM performs best.The results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

Biological Interaction and Imaging of Ultrasmall Gold Nanoparticles

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Nano-Micro Letters 2024年第3期16卷 69-98页

作者： Dongmiao Sang Xiaoxi luo Jinbin Liu Key Laboratory of Functional Molecular Engineering of Guangdong Province School of Chemistry and Chemical EngineeringSouth China University of TechnologyGuangzhou 510640People’s Republic of China

Ultrasmall gold nanoparticles(AuNPs)typically includes atomically precise gold nanoclusters(AuNCs)and AuNPs with a core size below 3 nm.Serving as a bridge between small molecules and traditional inorganic nanoparticles,the ultrasmall AuNPs show the unique advantages of both small molecules(e.g.,rapid distribution,renal clearance,low non-specific organ accumulation)and nanoparticles(e.g.,long blood circulation and enhanced permeability and retention effect).The emergence of ultrasmall AuNPs creates significant opportunities to address many challenges in the health field including disease diagnosis,monitoring and treatment.Since the nano–bio interaction dictates the overall biological applications of the ultrasmall AuNPs,this review elucidates the recent advances in the biological interactions and imaging of ultrasmall AuNPs.We begin with the introduction of the factors that influence the cellular interactions of ultrasmall AuNPs.We then discuss the organ interactions,especially focus on the interactions of the liver and kidneys.We further present the recent advances in the tumor interactions of ultrasmall AuNPs.In addition,the imaging performance of the ultrasmall AuNPs is summarized and discussed.Finally,we summarize this review and provide some perspective on the future research direction of the ultrasmall AuNPs,aiming to accelerate their clinical translation.

关键词： Ultrasmall gold nanoparticle Cellular interaction Organ interaction Tumor interaction Bioimaging

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Single-cell RNA sequencing in juvenile idiopathic arthritis

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Genes & Diseases 2024年第2期11卷 633-644页

作者： Xiwen luo Xuemei Tang Department of Rheumatology and Immunology Children’s Hospital of Chongqing Medical UniversityChongqing 400014China Chongqing Key Laboratory of Child Infection and Immunity Ministry of Education Key Laboratory of Child Development and DisordersNational Clinical Research Center for Child Health and DisordersChina International Science and Technology Cooperation Base of Child Development and Critical DisordersChildren's Hospital of Chongqing Medical UniversityChongqing 400014China

Juvenile idiopathic arthritis (JIA) is one of the most common chronic inflammatory rheumatic diseases in children,with onset before age 16 and lasting for more than 6 weeks.JIA is a highly heterogeneous condition with various consequences for health and quality of life.For some JIA patients,early detection and intervention remain challenging.As a result,further investigation of the complex and unknown mechanisms underlying JIA is required.Advances in technology now allow us to describe the biological heterogeneity and function of individual cell populations in JIA.Through this review,we hope to provide novel ideas and potential targets for the diagnosis and treatment of JIA by summarizing the current findings of single-cell RNA sequencing studies and understanding how the major cell subsets drive JIA pathogenesis.

关键词： Juvenile idiopathic arthritis Macrophage Macrophage activation syndrome Monocyte Single-cell RNA sequencing Synoviocyte T cell

Precision regulation of plant aromatic amino acid homeostasis

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Science China(Life Sciences) 2024年第6期67卷 1316-1317页

作者： Jie luo Yazhouwan National Laboratory(YNL) Sanya 572025China School of Breeding and Multiplication(Sanya Institute of Breeding and Multiplication) Hainan UniversitySanya 572025China

The aromatic amino acids(AAAs)phenylalanine(Phe),tyrosine(Tyr),and tryptophan(Trp)are not only building blocks of proteins,but also precursors of numerous primary and specialized metabolites(Schenck and Maeda,2018).For instance,Trp is a precursor of the phytohormone auxin and the plant defense compounds indole glucosinolates,while Phe serves as a precursor of lignin,coumarin and flavonoids.Some plant natural products such as morphine and vitamin E are derived from Tyr,and have been employed in human medicine and nutrition(Schenck and Maeda,2018;Wu et al.,2022;Yokoyama et al.,2021).

关键词： aromatic instance coumarin

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Microbes in mass extinction: an accomplice or a savior?

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National Science Review 2024年第1期11卷 12-15页

作者： Genming luo Deng Liu Hao Yang China University of Geosciences (Wuhan) State Key Laboratory of Geobiology and Environmental Geology

Microbes include various types of organisms that are invisible to the naked eye,e.g. bacteria, archaea and microeukaryotes. Although the precise number remains largely unknown, microbes are recognized as the unseen majority of our biosphere [1]. As a group of organisms classified by body size, microbes cover a considerable spectrum of metabolisms,

关键词： AFTERMATH

Large language model and domain-specific model collaboration for smart education

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 333-341页

作者： Yawei luo Yi YANG School of Software Technology Zhejiang UniversityNingbo 315048China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China

In this paper,we introduce the large language model and domain-specific model collaboration(LDMC)framework designed to enhance smart education.The LDMC framework leverages the comprehensive and versatile knowledge of large domain-general models,combines it with the specialized and disciplinary knowledge from small domainspecific models(DSMs),and incorporates pedagogy knowledge from learning theory models.This integration yields multiple knowledge representations,fostering personalized and adaptive educational experiences.We explore various applications of the LDMC framework in the context of smart education.

关键词： smart language knowledge

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