Journal of King Saud University-Computer and Information Sciences最新文献_第3页

On-chain zero-knowledge machine learning: An overview and comparison 链上零知识机器学习：概述与比较

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102207

Vid Keršič, Sašo Karakatič, Muhamed Turkanović

{"title":"On-chain zero-knowledge machine learning: An overview and comparison","authors":"Vid Keršič, Sašo Karakatič, Muhamed Turkanović","doi":"10.1016/j.jksuci.2024.102207","DOIUrl":"10.1016/j.jksuci.2024.102207","url":null,"abstract":"<div><div>Zero-knowledge proofs introduce a mechanism to prove that certain computations were performed without revealing any underlying information and are used commonly in blockchain-based decentralized apps (dapps). This cryptographic technique addresses trust issues prevalent in blockchain applications, and has now been adapted for machine learning (ML) services, known as Zero-Knowledge Machine Learning (ZKML). By leveraging the distributed nature of blockchains, this approach enhances the trustworthiness of ML deployments, and opens up new possibilities for privacy-preserving and robust ML applications within dapps. This paper provides a comprehensive overview of the ZKML process and its critical components for verifying ML services on-chain. Furthermore, this paper explores how blockchain technology and smart contracts can offer verifiable, trustless proof that a specific ML model has been used correctly to perform inference, all without relying on a single trusted entity. Additionally, the paper compares and reviews existing frameworks for implementing ZKML in dapps, serving as a reference point for researchers interested in this emerging field.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102207"},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IPSRM: An intent perceived sequential recommendation model IPSRM：意图感知顺序推荐模型

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102206

Chaoran Wang , Mingyang Wang , Xianjie Wang , Yingchun Tan

{"title":"IPSRM: An intent perceived sequential recommendation model","authors":"Chaoran Wang , Mingyang Wang , Xianjie Wang , Yingchun Tan","doi":"10.1016/j.jksuci.2024.102206","DOIUrl":"10.1016/j.jksuci.2024.102206","url":null,"abstract":"<div><h3>Objectives:</h3><div>Sequential recommendation aims to recommend items that are relevant to users’ interests based on their existing interaction sequences. Current models lack in capturing users’ latent intentions and do not sufficiently consider sequence information during the modeling of users and items. Additionally, noise in user interaction sequences can affect the model’s optimization process.</div></div><div><h3>Methods:</h3><div>This paper introduces an intent perceived sequential recommendation model (IPSRM). IPSRM employs the generalized expectation–maximization (EM) framework, alternating between learning sequence representations and optimizing the model to better capture the underlying intentions of user interactions. Specifically, IPSRM maps unlabeled behavioral sequences into frequency domain filtering and random Gaussian distribution space. These mappings reduce the impact of noise and improve the learning of user behavior representations. Through clustering process, IPSRM captures users’ potential interaction intentions and incorporates them as one of the supervisions into the contrastive self-supervised learning process to guide the optimization process.</div></div><div><h3>Results:</h3><div>Experimental results on four standard datasets demonstrate the superiority of IPSRM. Comparative experiments also verify that IPSRM exhibits strong robustness under cold start and noisy interaction conditions.</div></div><div><h3>Conclusions:</h3><div>Capturing latent user intentions, integrating intention-based supervision into model optimization, and mitigating noise in sequential modeling significantly enhance the performance of sequential recommendation systems.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102206"},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring 用于肿瘤生物标记物评分的全切片图像实时分割和分类

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-05 DOI: 10.1016/j.jksuci.2024.102204

Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan

{"title":"Real-time segmentation and classification of whole-slide images for tumor biomarker scoring","authors":"Md Jahid Hasan , Wan Siti Halimatul Munirah Wan Ahmad , Mohammad Faizal Ahmad Fauzi , Jenny Tung Hiong Lee , See Yee Khor , Lai Meng Looi , Fazly Salleh Abas , Afzan Adam , Elaine Wan Ling Chan","doi":"10.1016/j.jksuci.2024.102204","DOIUrl":"10.1016/j.jksuci.2024.102204","url":null,"abstract":"<div><div>Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102204"},"PeriodicalIF":5.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-stream dynamic graph structure network for document-level relation extraction 用于文档级关系提取的双流动态图结构网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-03 DOI: 10.1016/j.jksuci.2024.102202

Yu Zhong, Bo Shen

{"title":"Dual-stream dynamic graph structure network for document-level relation extraction","authors":"Yu Zhong, Bo Shen","doi":"10.1016/j.jksuci.2024.102202","DOIUrl":"10.1016/j.jksuci.2024.102202","url":null,"abstract":"<div><div>Extracting structured information from unstructured text is crucial for knowledge management and utilization, which is the goal of document-level relation extraction. Existing graph-based methods face issues with information confusion and integration, limiting the reasoning capabilities of the model. To tackle this problem, a dual-stream dynamic graph structural network is proposed to model documents from various perspectives. Leveraging the richness of document information, a static document heterogeneous graph is constructed. A dynamic heterogeneous document graph is then induced based on this foundation to facilitate global information aggregation for entity representation learning. Additionally, the static document graph is decomposed into multi-level static semantic graphs, and multi-layer dynamic semantic graphs are further induced, explicitly segregating information from different levels. Information from different streams is effectively integrated via an information integrator. To mitigate the interference of noise during the reasoning process, a noise regularization mechanism is also designed. The experimental results on three extensively utilized publicly accessible datasets for document-level relation extraction demonstrate that our model achieves F1 scores of 62.56%, 71.1%, and 86.9% on the DocRED, CDR, and GDA datasets, respectively, significantly outperforming the baselines. Further analysis also demonstrates the effectiveness of the model in multi-entity scenarios.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102202"},"PeriodicalIF":5.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ParaU-Net: An improved UNet parallel coding network for lung nodule segmentation ParaU-Net：用于肺结节分割的改进型 UNet 并行编码网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102203

Yingqi Lu , Xiangsuo Fan , Jinfeng Wang , Shaojun Chen , Jie Meng

{"title":"ParaU-Net: An improved UNet parallel coding network for lung nodule segmentation","authors":"Yingqi Lu , Xiangsuo Fan , Jinfeng Wang , Shaojun Chen , Jie Meng","doi":"10.1016/j.jksuci.2024.102203","DOIUrl":"10.1016/j.jksuci.2024.102203","url":null,"abstract":"<div><div>Accurate segmentation of lung nodules is crucial for the early detection of lung cancer and other pulmonary diseases. Traditional segmentation methods face several challenges, such as the overlap between nodules and surrounding anatomical structures like blood vessels and bronchi, as well as the variability in nodule size and shape, which complicates the segmentation algorithms. Existing methods often inadequately address these issues, highlighting the need for a more effective solution. To address these challenges, this paper proposes an improved multi-scale parallel fusion encoding network, ParaU-Net. ParaU-Net enhances the segmentation accuracy and model performance by optimizing the encoding process, improving feature extraction, preserving down-sampling information, and expanding the receptive field. Specifically, the multi-scale parallel fusion mechanism introduced in ParaU-Net better captures the fine features of nodules and reduces interference from other structures. Experiments conducted on the LIDC (The Lung Image Database Consortium) public dataset demonstrate the excellent performance of ParaU-Net in segmentation tasks, with results showing an IoU of 87.15%, Dice of 92.16%, F1-score of 92.24%, F2-score of 92.33%, and F0.5-score of 92.69%. These results significantly outperform other advanced segmentation methods, validating the effectiveness and accuracy of the proposed model in lung nodule CT image analysis. The code is available at <span><span>https://github.com/XiaoBai-Lyq/ParaU-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102203"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification LungNeXt：利用增强型 Mel 光谱图进行肺音分类的新型轻量级网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102200

Fan Wang , Xiaochen Yuan , Yue Liu , Chan-Tong Lam

{"title":"LungNeXt: A novel lightweight network utilizing enhanced mel-spectrogram for lung sound classification","authors":"Fan Wang , Xiaochen Yuan , Yue Liu , Chan-Tong Lam","doi":"10.1016/j.jksuci.2024.102200","DOIUrl":"10.1016/j.jksuci.2024.102200","url":null,"abstract":"<div><div>Lung auscultation is essential for early lung condition detection. Categorizing adventitious lung sounds requires expert discrimination by medical specialists. This paper details the features of LungNeXt, a novel classification model specifically designed for lung sound analysis. Furthermore, we propose two auxiliary methods: RandClipMix (RCM) for data augmentation and Enhanced Mel-Spectrogram for Feature Extraction (EMFE). RCM addresses the issue of data imbalance by randomly mixing clips within the same category to create new adventitious lung sounds. EMFE augments specific frequency bands in spectrograms to highlight adventitious features. These contributions enable LungNeXt to achieve outstanding performance. LungNeXt optimally integrates an appropriate number of NeXtblocks, ensuring superior performance and a lightweight model architecture. The proposed RCM and EMFE methods, along with the LungNeXt classification network, have been evaluated on the SPRSound dataset. Experimental results revealed a commendable score of 0.5699 for the lung sound five-category task on SPRSound. Specifically, the LungNeXt model is characterized by its efficiency, with only 3.804M parameters and a computational complexity of 0.659G FLOPS. This lightweight and efficient model is particularly well-suited for applications in electronic stethoscope back-end processing equipment, providing efficient diagnostic advice to physicians and patients.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102200"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-throughput systolic array-based accelerator for hybrid transformer-CNN networks 基于高通量收缩阵列的混合变压器-网络加速器

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102194

Qingzeng Song , Yao Dai , Hao Lu , Guanghao Jin

{"title":"High-throughput systolic array-based accelerator for hybrid transformer-CNN networks","authors":"Qingzeng Song , Yao Dai , Hao Lu , Guanghao Jin","doi":"10.1016/j.jksuci.2024.102194","DOIUrl":"10.1016/j.jksuci.2024.102194","url":null,"abstract":"<div><div>In this era of Transformers enjoying remarkable success, Convolutional Neural Networks (CNNs) remain highly relevant and useful. Indeed, hybrid Transformer-CNN network architectures, which combine the benefits of both approaches, have achieved impressive results. Vision Transformer (ViT) is a significant neural network architecture that features a convolutional layer as its first layer, primarily built on the transformer framework. However, owing to the distinct computation patterns inherent in attention and convolution, existing hardware accelerators for these two models are typically designed separately and lack a unified approach toward accelerating both models efficiently. In this paper, we present a dedicated accelerator on a field-programmable gate array (FPGA) platform. The accelerator, which integrates a configurable three-dimensional systolic array, is specifically designed to accelerate the inferential capabilities of hybrid Transformer-CNN networks. The Convolution and Transformer computations can be mapped to a systolic array by unifying these operations for matrix multiplication. Softmax and LayerNorm which are frequently used in hybrid Transformer-CNN networks were also implemented on FPGA boards. The accelerator achieved high performance with a peak throughput of 722 GOP/s at an average energy efficiency of 53 GOPS/W. Its respective computation latencies were 51.3 ms, 18.1 ms, and 6.8 ms for ViT-Base, ViT-Small, and ViT-Tiny. The accelerator provided a <span><math><mrow><mn>12</mn><mo>×</mo></mrow></math></span> improvement in energy efficiency compared to the CPU, a <span><math><mrow><mn>2</mn><mo>.</mo><mn>3</mn><mo>×</mo></mrow></math></span> improvement compared to the GPU, and a <span><math><mrow><mn>1</mn><mo>.</mo><mn>5</mn><mo>×</mo></mrow></math></span> to <span><math><mrow><mn>2</mn><mo>×</mo></mrow></math></span> improvement compared to existing accelerators regarding speed and energy efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102194"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A scalable attention network for lightweight image super-resolution 用于轻量级图像超分辨率的可扩展注意力网络

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102185

Jinsheng Fang , Xinyu Chen , Jianglong Zhao , Kun Zeng

{"title":"A scalable attention network for lightweight image super-resolution","authors":"Jinsheng Fang , Xinyu Chen , Jianglong Zhao , Kun Zeng","doi":"10.1016/j.jksuci.2024.102185","DOIUrl":"10.1016/j.jksuci.2024.102185","url":null,"abstract":"<div><div>Modeling long-range dependencies among features has become a consensus to improve the results of single image super-resolution (SISR), which stimulates interest in enlarging the kernel sizes in convolutional neural networks (CNNs). Although larger kernels definitely improve the network performance, network parameters and computational complexities are raised sharply as well. Hence, an optimization of setting the kernel sizes is required to improve the efficiency of the network. In this work, we study the influence of the positions of larger kernels on the network performance, and propose a scalable attention network (SCAN). In SCAN, we propose a depth-related attention block (DRAB) that consists of several multi-scale information enhancement blocks (MIEBs) and resizable-kernel attention blocks (RKABs). The RKAB dynamically adjusts the kernel size concerning the locations of the DRABs in the network. The resizable mechanism allows the network to extract more informative features in shallower layers with larger kernels and focus on useful information in deeper layers with smaller ones, which effectively improves the SR results. Extensive experiments demonstrate that the proposed SCAN outperforms other state-of-the-art lightweight SR methods. Our codes are available at <span><span>https://github.com/ginsengf/SCAN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102185"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing requirements-to-code traceability with GA-XWCoDe: Integrating XGBoost, Node2Vec, and genetic algorithms for improving model performance and stability 利用 GA-XWCoDe 增强从需求到代码的可追溯性：集成 XGBoost、Node2Vec 和遗传算法，提高模型性能和稳定性

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-10-01 DOI: 10.1016/j.jksuci.2024.102197

Zhiyuan Zou , Bangchao Wang , Xinrong Hu , Yang Deng , Hongyan Wan , Huan Jin

{"title":"Enhancing requirements-to-code traceability with GA-XWCoDe: Integrating XGBoost, Node2Vec, and genetic algorithms for improving model performance and stability","authors":"Zhiyuan Zou , Bangchao Wang , Xinrong Hu , Yang Deng , Hongyan Wan , Huan Jin","doi":"10.1016/j.jksuci.2024.102197","DOIUrl":"10.1016/j.jksuci.2024.102197","url":null,"abstract":"<div><div>This study addresses the challenge of requirements-to-code traceability by proposing a novel model, Genetic Algorithm-XGBoost With Code Dependency (GA-XWCoDe), which integrates eXtreme Gradient Boosting (XGBoost) with a Node2Vec model-weighted code dependency strategy and genetic algorithms for parameter optimisation. XGBoost mitigates overfitting and enhances model stability, while Node2Vec improves prediction accuracy for low-confidence links. Genetic algorithms are employed to optimise model parameters efficiently, reducing the resource intensity of traditional methods. Experimental results show that GA-XWCoDe outperforms the state-of-the-art method TRAceability lInk cLassifier (TRAIL) by 17.44% and Deep Forest for Requirement traceability (DF4RT) by 33.36% in terms of average F1 performance across four datasets. It is significantly superior to all baseline methods at a confidence level of <span><math><mi>α</mi></math></span>¡0.01 and demonstrates exceptional performance and stability across various training data scales.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 8","pages":"Article 102197"},"PeriodicalIF":5.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast and robust JND-guided video watermarking scheme in spatial domain 空间域快速稳健的 JND 引导视频水印方案

IF 5.2 2区计算机科学

Journal of King Saud University-Computer and Information Sciences Pub Date : 2024-09-30 DOI: 10.1016/j.jksuci.2024.102199

Antonio Cedillo-Hernandez , Lydia Velazquez-Garcia , Manuel Cedillo-Hernandez , David Conchouso-Gonzalez

{"title":"Fast and robust JND-guided video watermarking scheme in spatial domain","authors":"Antonio Cedillo-Hernandez , Lydia Velazquez-Garcia , Manuel Cedillo-Hernandez , David Conchouso-Gonzalez","doi":"10.1016/j.jksuci.2024.102199","DOIUrl":"10.1016/j.jksuci.2024.102199","url":null,"abstract":"<div><div>Generally speaking, those watermarking studies using the spatial domain tend to be fast but with limited robustness and imperceptibility while those performed in other transform domains are robust but have high computational cost. Watermarking applied to digital video has as one of the main challenges the large amount of computational power required due to the huge amount of information to be processed. In this paper we propose a watermarking algorithm for digital video that addresses this problem. To increase the speed, the watermark is embedded using a technique to modify the DCT coefficients directly in the spatial domain, in addition to carrying out this process considering the video scene as the basic unit and not the video frame. In terms of robustness, the watermark is modulated by a Just Noticeable Distortion (JND) scheme computed directly in the spatial domain guided by visual attention to increase the strength of the watermark to the maximum level but without this operation being perceivable by human eyes. Experimental results confirm that the proposed method achieves remarkable performance in terms of processing time, robustness and imperceptibility compared to previous studies.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102199"},"PeriodicalIF":5.2,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0