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Adversarial multi-image steganography via texture evaluation and multi-scale image enhancement

Multimedia Tools and Applications 2025年第9期84卷 5793-5823页

作者： Li, Fengyong Li, Longwei Zeng, Yishu Yu, Jiang Qin, Chuan College of Computer Science and Technology Shanghai University of Electric Power Shanghai201306 China School of Information and Computer Shanghai Business School Shanghai201400 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200092 China

Multi-image steganography refers to a data-hiding scheme where a user tries to hide confidential messages within multiple images. Different from the traditional steganography which only requires the security of an individual image, multi-image steganography considers an overall security for a batch of images. However, existing multi-image steganography all faces a nontrivial problem: how to optimally allocate payload into multiple images to guarantee the security of batch images. To address this problem, this paper proposes an adversarial multi-image steganographic scheme. A multi-scale texture evaluation mechanism is firstly calculated to determine the embeddable cover images. Subsequently, a series of multi-scale filters are introduced to enhance the image content, which can be used to guide the optimal payload assignment of each image. Furthermore, an adversarial embedding mechanism is designed by dynamically adjusting the random gradient mapping of batch images, and finally achieving secure multi-image steganography. Our proposed scheme can optimize the overall steganographic security performance of multiple images, while ensuring the anti-steganalysis capability of a single image. Extensive experiments demonstrate that our scheme can achieve superior performance for multi-image steganography over different large-scale image sets, and outperforms state-of-the art schemes in terms of both single-image and multi-image anti-steganalysis capability. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Embeddings

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Few-shot image semantic segmentation based on meta-learning: A review

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 351-367页

作者： Liu, Yu Zhu, Ye Chong, Haoze Yu, Ming School of Electronic and Information Engineering Hebei University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer and Information Engineering Tianjin Agricultural University Tianjin China

Deep learning-based image semantic segmentation approaches heavily rely on large-scale training datasets with dense annotations and often suffer from scarce semantic labels for unseen categories. This limitation has spurred a research trend in Few-shot image Semantic Segmentation (FSS), which makes it possible to segment objects of new categories using only a few labeled samples. Although more and more FSS methods are emerging and gradually integrated into practical applications, a deep understanding of its achievements and issues is still missing. In this survey, we focus on the recent developments of FSS, specifically on FSS methods based on meta-learning. According to different network architectures, we summarize the related research into three classes, that are Convolutional Neural Network-based (CNN-based) models, Graph Neural Network-based (GNN-based) models, and Transformer-based models. Then, we explore the specific implementations of these models, including parameter-based methods, metric-based methods, attention-based methods, and optimization-based methods. Furthermore, we illustrate datasets and analyze the experimental results of various kinds of methods. Toward the end of the paper, we discuss the limitations of FSS and present its applications and challenges to provide further research directions. © 2024 - IOS Press. All rights reserved.

关键词： Semantic Segmentation

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An Intelligent Shared Decision-Making Model among One Patient and Multiple Doctors

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第1期8卷 132-156页

作者： Liu, Yong Lin, Kai-Biao Lu, Ping Lin, Min School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Electromechanical and Information Engineering Putian University Putian351100 China

Shared decision-making (SDM) is an effective decision-making method in clinical practice. However, the pressure of negotiation and decision makes it difficult to apply widely. To alleviate the pressure of artificial SDM and promote the realization of clinical SDM, this article presents a fuzzy constraint-based negotiation and decision method for the patient-doctors SDM. The proposed method includes a negotiation model and a decision-making model. The negotiation model quantifies the negotiation process between patient agent (PA) and doctor agents (DAs) in SDM. It consists of the negotiation behavior and the negotiation protocol of agents. The decision-making model quantifies the decision process of SDM. It translates the negotiation results into treatment plans and assists PA in making decisions. The main contributions are as follows: 1) the agent technology is applied to make one-to-many SDM efficient and intelligent;2) the distributed and fuzzy constraint theories are used to design an interconnected, autonomous, and distributed multi-agents negotiation system for SDM;3) the decision-making model is presented to assist doctors and patients in making decisions. The evaluation results of the negotiation and decision models demonstrate that our method is feasible and effective. © 2023, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Decision making

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SSF: Sparse point cloud object detection based on self-adaptive voxel encoding and focal-sparse convolution

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11041-11054页

作者： Zhang, Yu Wang, Zilong Zhu, Yongjian Li, Jianxin School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China College of Engineering Physics Shenzhen Technology University Guangdong Province Shenzhen China

Point cloud object detection is gradually playing a key role in autonomous driving tasks. To address the issue of insensitivity to sparse objects in point cloud object detection, we have made improvements to the voxel encoding and 3D backbone network of the PVRCNN++. We have introduced adaptive pooling operations during voxel feature encoding to expand the point cloud information within each voxel, followed by the utilization of multi-layer perceptrons to extract richer point cloud features. On the 3D backbone network, we have employed adaptive sparse convolution operations to make the backbone network's channel count more flexible, allowing it to accommodate a wider range of input data types. Furthermore, we have integrated Focal Loss to tackle the issue of class imbalance in detection tasks. Experimental results on the public KITTI dataset demonstrate significant improvements over the PVRCNN++, particularly in pedestrian and bicycle detection tasks. Specifically, we have observed 1% increase in detection accuracy for pedestrians and 2.1% improvement for bicycles. Our detection performance also surpasses that of other comparative detection algorithms. © 2024 - IOS Press. All rights reserved.

关键词： Object detection

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Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey

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computer Modeling in engineering & Sciences 2024年第3期138卷 2061-2102页

作者： Hong Zhang Haijian Shao School of Electrical Information Engineering Jiangsu University of TechnologyChangzhou213001China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA

OpenAI and ChatGPT, as state-of-the-art languagemodels driven by cutting-edge artificial intelligence technology,have gained widespread adoption across diverse industries. In the realm of computer vision, these models havebeen employed for intricate tasks including object recognition, image generation, and image processing, leveragingtheir advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have foundutility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactiveplayer experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providinginvaluable insights and support to healthcare professionals in the realmof disease detection. The principal objectiveof this paper is to offer a comprehensive overview of OpenAI, OpenAI Gym, ChatGPT, DALL E, stable diffusion,the pre-trained clip model, and other pertinent models in various domains, encompassing CLIP Text-to-Image,education, medical imaging, computer vision, social influence, natural language processing, software development,coding assistance, and Chatbot, among others. Particular emphasis will be placed on comparative analysis andexamination of popular text-to-image and text-to-video models under diverse stimuli, shedding light on thecurrent research landscape, emerging trends, and existing challenges within the domains of OpenAI and *** a rigorous literature review, this paper aims to deliver a professional and insightful overview of theadvancements, potentials, and limitations of these pioneering language models.

关键词： OpenAI ChatGPT DALL E stable diffusion OpenAI Gym text-to-image text-to-video

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DAUNet: Detail-Aware U-Shaped Network for 2D Human Pose Estimation

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computers, Materials & Continua 2024年第11期81卷 3325-3349页

作者： Xi Li Yuxin Li Zhenhua Xiao Zhenghua Huang Lianying Zou College of Information and Artificial Intelligence Nanchang Institute of Science and TechnologyNanchang330108China School of Electrical and Information Engineering Wuhan Institute of TechnologyWuhan430205China School of Computer Science and Technology Hubei Business CollegeWuhan430079China

Human pose estimation is a critical research area in the field of computer vision,playing a significant role in applications such as human-computer interaction,behavior analysis,and action *** this paper,we propose a U-shaped keypoint detection network(DAUNet)based on an improved ResNet subsampling structure and spatial grouping *** network addresses key challenges in traditional methods,such as information loss,large network redundancy,and insufficient sensitivity to low-resolution *** is composed of three main ***,we introduce an improved BottleNeck block that employs partial convolution and strip pooling to reduce computational load and mitigate feature ***,after upsampling,the network eliminates redundant features,improving the overall ***,a lightweight spatial grouping attention mechanism is applied to enhance low-resolution semantic features within the feature map,allowing for better restoration of the original image size and higher *** results demonstrate that DAUNet achieves superior accuracy compared to most existing keypoint detection models,with a mean PCKh@0.5 score of 91.6%on the MPII dataset and an AP of 76.1%on the COCO ***,real-world experiments further validate the robustness and generalizability of DAUNet for detecting human bodies in unknown environments,highlighting its potential for broader applications.

关键词： Human pose estimation keypoint detection U-shaped network architecture spatial grouping mechanism

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A voting-based trustworthy distributed IoT attack detection model

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Personal and Ubiquitous Computing 2025年第1期29卷 103-118页

作者： Sharma, Priya Sharma, Sanjay Kumar Dani, Diksha University School of Information & Communication Technology Gautam Buddha University Greater Noida India Department of Computer Engineering SVKM’s NMIMS Mukesh Patel School of Technology Management & Engineering Mumbai India

Besides the enhancement of the Internet of Things (IoT) distributed environment, anomalous activities are also escalating rapidly. Therefore, improving the trustworthiness of distributed networks is required for the extensive adoption of IoT infrastructure. Establishing a security mechanism in IoT networks is a challenging task as communication links are lossy and connected devices are resource-dependent. Conventional security techniques such as intrusion detection systems (IDS) are insufficient to shelter the IoT-distributed environment due to less computational capacity, restricted upgraded devices, and mismatched protocols. This paper proposes a novel machine learning-based trustworthy model for IoT attack detection. The proposed system combines the capability of Ada-boost and Gradient-boost to classify anomalous activities with low computational capacity proficiently and within a minimum time frame. Experiments were conducted on Distributed Smart Space Orchestration System (DS2OS) IoT dataset to assess the significance of the novel attack detection model. The demonstration shows that the proposed model obtains 98.28% training accuracy, 98.26% testing accuracy, and 99.25% AUC score. A confusion matrix has also been calculated to check the authenticity of the results—a comparison of the proposed model with individual learners and existing models proves that the proposed model outperformed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Network intrusion

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TransFuseNetS: A new Image Medical Segmentation Method based on TransFuse Architecture 24

TransFuseNetS: A new Image Medical Segmentation Method based...

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2024 International Conference on computer Vision and Deep Learning, CVDL 2024

作者： Chen, Wei Wang, Zhiheng School of Computer and Information Engineering Heilongjiang University of Science and Technology Heilongjiang China School of Computer and Information Engineering Heilongjiang University of Science and Technology Anhui China

ISBN: (纸本)9798400718199

In medical images, image segmentation is a very important method, which can accurately locate and analyze the lesions and tissues. However, due to the complexity of medical images and noise, accurate and robust segmentation is still a difficult problem. In view of the above problems, this project intends to use TransFuseNetS under the framework of Transfuse for image medical segmentation. TransFuseNetS further optimizes and strengthens the Fusion architecture. First of all, this project intends to design a more complex coding architecture and propose an overall attention strategy at the channel level to improve the feature representation and understandability of the image. Secondly, the linear fusion method of two directions is used to make the multi-level images more comprehensive and rich, so as to improve the identification and segmentation of multi-scale images. Through a large number of experiments, we find that TransFuseNetS can effectively improve the recognition rate of CT images. Compared with the traditional artificial neural network, the average error of IOU and ACC of artificial neural network is reduced by 0.1571, and the average error is reduced by 0.0029. The research results of this project will lay a foundation for the research and application in the fields of medical image processing and medical image processing. © 2024 ACM.

关键词： Image segmentation

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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection

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computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： Oriented object detection transformer deep learning

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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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