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A robust watermarking scheme for hybrid attacks on heritage images

Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7367-7380页

作者： Mehraj, Samrah Mushtaq, Subreena Parah, Shabir A. Giri, Kaiser J. Sheikh, Javaid A. Department of Electronics and Instrumentation Technology University of Kashmir Srinagar190006 India Department of Computer Science Islamic University of Science and Technology Pulwama192122 India

Heritage incorporates images, customs, rituals, beliefs, cultures, knowledge, arts, crafts, music, and artifacts of a region. Apart from conferring the details about the understanding of previous generations, the heritage multimedia also provides details regarding innovative attitudes, ways of living, and heterogeneity of historical and archaeological approaches of a society. Since the availability of the internet in the present scenario makes an illicit user access the data easily, therefore, it is mandatory to protect the Cultural Heritage (CH) media. This paper offers a secure and resilient Discrete Cosine Transform (DCT) based blind watermarking algorithm for copyright security of digital images. We have developed and tested two different approaches of embedding namely Fixed Threshold Reference-based Relative Modulation (FTRRM) and Adaptive Threshold Reference-based Relative Modulation (ATRRM). In either approach, the watermark is embedded in the ‘Y’ component of the YCbCr color model. In FTRRM, the ‘Y’ component of the host image is first divided into 8 × 8 non-overlapping blocks and DCT is applied on each block. The watermark bits are embedded into the transformed coefficients by modulating the relative difference of coefficients depending on the bit to be embedded. A similar embedding strategy is followed in ATRRM, with a change that relative difference is modulated adaptively. Both schemes have been tested on a set of heritage images, besides general test images. We make use of chaotic and Deoxyribo Nucleic acid (DNA) encryption to ensure double-layer security of embedded watermark. The Peak Signal-to-Noise Ratio (PSNR) of the proposed scheme in the case of FTRRM is 40.4984 dB and for ATRRM it is 39.9549 dB, the Structural Similarity Index Matrix (SSIM) values are close to unity, in both cases for various test images that guarantee the imperceptibility of the proposed scheme. The robustness of each scheme is revealed by comparing them with the various state-

关键词： Discrete cosine transforms

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APENet: Adaptive Pyramid Pooling and SE Network for Superpixel Segmentation 4

APENet: Adaptive Pyramid Pooling and SE Network for Superpix...

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4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Dang, Yuanyuan Gao, Yu Liu, Bing Li, Chenxi College of Computer Science and Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9798350326444

Superpixel segmentation is a commonly used computer vision image segmentation method that aims to cluster similar pixels in an image into larger, more meaningful regions. Traditional superpixel segmentation algorithms are non-differentiable and usually hard to extract multi-scale features. To solve this problem, this paper proposes Adaptive Pyramid Pooling Module (APPM) and SE attention module (SEM) for the superpixel segmentation algorithm. APPM can introduce the multi-scale feature, And SEM can add the channel interaction into the superpixel feature. Experimental results show that the algorithm can achieve state-of-the-art performance in the BSDS500 dataset. © 2023 IEEE.

关键词： Superpixels

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KidneyMultiNet: A Web-Based Automatic System for Kidney Disease Detection Using Hybrid Machine Learning Model From CT Scan Images

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Biomedical Materials and Devices 2024年 1-12页

作者： Rana, Sahab Uddin Nur-A-Alam, Md. Akter, Sadeka Likhon, Md. Nur Hosain Department of Computer Science and Engineering Dhaka University of Engineering & amp Technology Gazipur Dhaka Bangladesh Department of Computer Science and Engineering Sunamgonj Science and Technology University Sylhet Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka Bangladesh

Chronic renal disease is the term used to describe kidney function that gradually declines. The kidneys’ final byproduct of eliminating waste and surplus fluid from the bloodstream is urine. Abnormal accumulations of fluid, electrolytes, and waste products can occur in the body when chronic renal disease progresses to a critical degree. Nonetheless, kidney disease has been predicted using various artificial machine learning algorithm approaches. In this study, authors present the “KidneyMultiNet” framework, which combines two models of convolutional neural networks Densenet201 and Xception, which are based on the idea of transfer learning and can predict kidney disease based on kidney CT scan datasets. The “KidneyMultiNet” design that has been suggested makes a thorough and reasonable diagnosis of kidney disease in the kidney conceivable. The authors used a publicly available kidney CT scan image dataset to train and test the proposed model. To determine the efficacy of the output from suggested advanced machine learning CNN methods, the author fed them picture data using a train generator. Then, the author used a rate of learning to decrease the datasets within the proposed model. After completing various learning procedures with many kidney pictures, the proposed model could accurately predict kidney disease, reaching a maximum performance of 99.92%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural network Image processing Kidney disease Machine learning

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A hierarchical path planning algorithm based on graphical search and optimization methods 3

A hierarchical path planning algorithm based on graphical se...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Wu, Liang Yi, Chengzhi Cheng, Chao Xie, Dongxuan The School of Computer Science and Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9781510668522

This paper proposes a hierarchical path-planning algorithm based on a combination of graphical search algorithms and optimization methods. In the global path planning layer, by using the Hybrid A∗ algorithm, we can quickly obtain an optimal path that can avoid all static obstacles on the map. In the local path planning layer, the global path is optimized by numerical nonlinear numerical optimization to generate a feasible path that satisfies the safety constraints. By processing global path planning and local path planning separately, the computational complexity of path planning can be effectively reduced, and the efficiency and accuracy of the path planning can be improved. Secondly, the hybrid planning algorithm can generate high-quality paths with both safety and flexibility. We simulate and verify the algorithm, and the results show that the method has practical applications in autonomous driving. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Motion planning

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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LDFUNet: Large-Kernel Convolution and Dynamic Fusion Network for Superpixel Segmentation 4

LDFUNet: Large-Kernel Convolution and Dynamic Fusion Network...

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4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Dang, Yuanyuan Wei, Junheng Zhang, Lijie Zhong, Zhaohao College of Computer Science and Engineering Changchun University of Technology Changchun China

ISBN: (纸本)9798350326444

Superpixel is an image preprocessing technique that combines adjacent pixels in an image to form smaller connected regions. It serves as the smallest unit for subsequent image-processing tasks. Reduced computational complexity and improved accuracy of image segmentation. The past superpixel segmentation algorithms need to compute the hard associate map between pixels and seeds, which results in a huge computation cost. To reduce the computation cost, we propose LDFUNet, Large-Kernel Convolution, and Dynamic Fusion Network. Large-Kernel Convolution extract superpixel feature efficiently. And dynamic can fuse the pixel and superpixel feature dynamic, which can capture the pixel-superpixel relationship information dynamic for the final prediction. The experiments show that our method achieves a good trade-off between computational speed and segmentation accuracy. © 2023 IEEE.

关键词： Superpixels

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Adversarial Modality Alignment Network for Cross-Modal Molecule Retrieval

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 278-289页

作者： Zhao, Wenyu Zhou, Dong Cao, Buqing Zhang, Kai Chen, Jinjun Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan425100 China Swinburne University of Technology Department of Computing Technologies MelbourneVIC3122 Australia Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510420 China

The cross-modal molecule retrieval (Text2Mol) task aims to bridge the semantic gap between molecules and natural language descriptions. A solution to this nontrivial problem relies on a graph convolutional network (GCN) and cross-modal attention with contrastive learning for reasonable results. However, there exist the following issues. First, the cross-modal attention mechanism is only in favor of text representations and cannot provide helpful information for molecule representations. Second, the GCN-based molecule encoder ignores edge features and the importance of various substructures of a molecule. Finally, the retrieval learning loss function is rather simplistic. This article further investigates the Text2Mol problem and proposes a novel adversarial modality alignment network (AMAN) based method to sufficiently learn both description and molecule information. Our method utilizes a SciBERT as a text encoder and a graph transformer network as a molecule encoder to generate multimodal representations. Then, an adversarial network is used to align these modalities interactively. Meanwhile, a triplet loss function is leveraged to perform retrieval learning and further enhance the modality alignment. Experiments on the ChEBI-20 dataset show the effectiveness of our AMAN method compared with baselines. © 2020 IEEE.

关键词： Semantics

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Navigating Anxiety in a Cloud E-learning Virtual Environments: The Moderating Role of Anxiety on Lecturers’ Adoption of Cloud Computing E-learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第17期18卷 115-138页

作者： Armah, Elisha D’Archimedes Sathik Ali, I. Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Technology Cape Coast Technical University Cape Coast Ghana Department of Information Technology B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India

The rapid expansion of cloud computing e-learning systems has empowered educators to reach learners worldwide. However, a critical study gap exists: the impact of lecturers’ technological anxiety on their adoption intentions remains unexplored in the technical universities in Ghana. This study addresses this gap by investigating anxiety’s influence on lecturers’ cloud computing e-learning adoption intentions within a virtual environment (VE). We examine the mediating role of competitive pressure and the moderating role of anxiety. Using PLSSEM, we analysed data from surveys conducted among 1395 lecturers across all 10 technical universities. With a high response rate of 90.2% (n = 1258), our analysis reveals moderate anxiety among lecturers regarding cloud e-learning adoption. The proposed model explains 21.1% of the variance in adoption intentions. Interestingly, competitive pressure on lecturers mediates the relationship between cloud e-learning usefulness, security, and adoption decisions. Additionally, anxiety moderates the effects of cloud computing complexity, security, and adoption intentions. We have recommended organizing regular training workshops, ensuring user-friendly platforms, implementing robust security measures, and providing psychological support for lecturers using cloud computing technologies. © 2024 by the authors of this article.

关键词： Virtual environments

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A Research on Perceived Farm Interaction System Based on Machine Learning 23

A Research on Perceived Farm Interaction System Based on Mac...

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7th International Conference on Big Data and Internet of Things, BDIOT 2023

作者： Kang, Huzhaorui College of Computer Science and Technology Changchun Normal University Changchun China

ISBN: (纸本)9798400708015

This paper presents a solution to the challenges in agriculture by designing a machine learning based farm interaction system that can accurately detect the maturity of farm produce in real-Time and remotely control the farm environment in a narrowband IoT (Internet of Things) environment. The study focuses on apple ripeness as an example, and utilizes a web crawler tool to gather images of young, growing, and ripe apples. These images were then processed using data enhancement methods to create a comprehensive data set. The YOLO-v3DenseNet target detection model was used to train and test the data set. Finally, a web system was designed using the Vue and Flask frameworks and deployed to a cloud server. The experimental test results indicate that the system allows users to upload photos of farm crops or access real-Time videos or photos of farms collected through the cloud, while displaying their maturity and confidence levels. The YOLO-v3DenseNet model used in the system can effectively detect the maturity of crops, even when crops overlap or are partially obscured. The average detection accuracy of this model for target feature images of crop maturity exceeds 90%, and can be applied to real-world farm environments. © 2023 ACM.

关键词： Machine learning

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Enhanced Physical Layer Security for Full-Duplex Symbiotic Radio with AN Generation and Forward Noise Suppression

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IEEE Transactions on Communications 2024年第7期72卷 3905-3918页

作者： Jin, Chi Chang, Zheng Hu, Fengye Chen, Hsiao-Hwa Hamalainen, Timo University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Jilin University College of Communication Engineering Changchun130012 China National Cheng Kung University Department of Engineering Science Tainan70101 Taiwan

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance. 0090-6778 © 2024 IEEE.

关键词： Backscattering

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