Journal of King Saud University-Computer and Information Sciences最新文献

{"title":"Real-time semantic segmentation for autonomous driving: A review of CNNs, Transformers, and Beyond","authors":"Mohammed A.M. Elhassan ,&nbsp;Changjun Zhou ,&nbsp;Ali Khan ,&nbsp;Amina Benabid ,&nbsp;Abuzar B.M. Adam ,&nbsp;Atif Mehmood ,&nbsp;Naftaly Wambugu","doi":"10.1016/j.jksuci.2024.102226","DOIUrl":"10.1016/j.jksuci.2024.102226","url":null,"abstract":"<div><div>Real-time semantic segmentation is a crucial component of autonomous driving systems, where accurate and efficient scene interpretation is essential to ensure both safety and operational reliability. This review provides an in-depth analysis of state-of-the-art approaches in real-time semantic segmentation, with a particular focus on Convolutional Neural Networks (CNNs), Transformers, and hybrid models. We systematically evaluate these methods and benchmark their performance in terms of frames per second (FPS), memory consumption, and CPU runtime. Our analysis encompasses a wide range of architectures, highlighting their novel features and the inherent trade-offs between accuracy and computational efficiency. Additionally, we identify emerging trends, and propose future directions to advance the field. This work aims to serve as a valuable resource for both researchers and practitioners in autonomous driving, providing a clear roadmap for future developments in real-time semantic segmentation. More resources and updates can be found at our GitHub repository: <span><span>https://github.com/mohamedac29/Real-time-Semantic-Segmentation-Survey</span><svg><path></path></svg></span></div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102226"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657830","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}

{"title":"Visually meaningful image encryption for secure and authenticated data transmission using chaotic maps","authors":"Deep Singh ,&nbsp;Sandeep Kumar ,&nbsp;Chaman Verma ,&nbsp;Zoltán Illés ,&nbsp;Neerendra Kumar","doi":"10.1016/j.jksuci.2024.102235","DOIUrl":"10.1016/j.jksuci.2024.102235","url":null,"abstract":"<div><div>Image ciphering techniques usually transform a given plain image data into a cipher image data resembling noise, serving as an indicator of the presence of secret image data. However, the transmission of such noise-like images could draw attention, thereby attracting the attackers and may face several possible attacks. This paper presents an approach for generating a visually meaningful image encryption (VMIE) scheme that combines three layers of security protection: encryption, digital signature, and steganography. The present scheme is dedicated to achieving a balanced performance in robustness, security and operational efficiency. First, the original image is partially encrypted by using the RSA cryptosystem and modified Hénon map (MHM). In the second stage, a digital signature is generated for the partially encrypted image by employing a hash function and the RSA cryptosystem. The obtained digital signature is appended to the partially encrypted image produced after implementing the zigzag confusion in the above partially encrypted image. Further, to achieve better confusion and diffusion, the partially encrypted image containing a digital signature undergoes through the application of 3D Arnold cat map (<span><math><mrow><mi>A</mi><msub><mrow><mi>R</mi></mrow><mrow><mi>n</mi><mi>o</mi></mrow></msub></mrow></math></span> times), to produce the secret encrypted image <span><math><mrow><mo>(</mo><msub><mrow><mi>S</mi></mrow><mrow><mi>r</mi><mn>5</mn></mrow></msub><mo>)</mo></mrow></math></span>. To ensure the security and robustness of the proposed technique against various classical attacks, the hash value obtained from the SHA-256 hash function and carrier images is utilized to generate the initial conditions <span><math><mrow><mi>M</mi><msub><mrow><mi>h</mi></mrow><mrow><mn>10</mn></mrow></msub></mrow></math></span> and <span><math><mrow><mi>M</mi><msub><mrow><mi>h</mi></mrow><mrow><mn>20</mn></mrow></msub></mrow></math></span> for modified Hénon map, and initial position <span><math><mrow><msub><mrow><mi>Z</mi></mrow><mrow><mi>i</mi><mi>p</mi></mrow></msub><mo>=</mo><mrow><mo>(</mo><msub><mrow><mi>z</mi></mrow><mrow><mi>r</mi><mi>o</mi><mi>w</mi></mrow></msub><mo>,</mo><msub><mrow><mi>z</mi></mrow><mrow><mi>c</mi><mi>o</mi><mi>l</mi></mrow></msub><mo>)</mo></mrow></mrow></math></span> for zigzag confusion. In the proposed algorithm, the digital signature is utilized for both purposes to verify the sender’s authenticity and to enhance the encryption quality. The carrier image undergoes lifting wavelet transformation, and its high-frequency components are utilized in the embedding process through a permuted pattern of MHM, resulting in a visually meaningful encrypted image. The proposed scheme achieves efficient visual encryption with minimal distortion and ensures lossless image quality upon decryption (infinite PSNR), balancing high level of security along with a good computational efficiency.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102235"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657829","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}

{"title":"Leukocyte segmentation based on DenseREU-Net","authors":"Jie Meng ,&nbsp;Yingqi Lu ,&nbsp;Wangjiao He ,&nbsp;Xiangsuo Fan ,&nbsp;Gechen Zhou ,&nbsp;Hongjian Wei","doi":"10.1016/j.jksuci.2024.102236","DOIUrl":"10.1016/j.jksuci.2024.102236","url":null,"abstract":"<div><div>The detection of white blood cells provides important information in cellular research regarding infections, inflammation, immune function, and blood pathologies. Effective segmentation of WBCs in blood microscopic images not only aids pathologists in making more accurate diagnoses and early detections but is also crucial for identifying the types of lesions. Due to significant differences among various types of pathological WBCs and the complexities associated with cellular imaging and staining techniques, accurately recognizing and segmenting these different types of WBCs remains challenging. To address these challenges, this paper proposes a WBC segmentation technique based on DenseREU-Net, which enhances feature exchange and reuse by employing Dense Blocks and residual units. Additionally, it introduces mixed pooling and skip multi-scale fusion modules to improve the recognition and segmentation accuracy of different types of pathological WBCs. This study was validated on two datasets provided by DML-LZWH (Liuzhou Workers’ Hospital Medical Laboratory). Experimental results indicate that on the multi-class dataset, DenseREU-Net achieves an average IoU of 73.05% and a Dice coefficient of 80.25%. For the binary classification blind sample dataset, the average IoU and Dice coefficient are 83.98% and 90.41%, respectively. In both datasets, the proposed model significantly outperforms other advanced medical image segmentation algorithms. Overall, DenseREU-Net effectively analyzes blood microscopic images and accurately recognizes and segments different types of WBCs, providing robust support for the diagnosis of blood-related diseases.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102236"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657923","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}

{"title":"LMGA: Lightweight multi-graph augmentation networks for safe medication recommendation","authors":"Xingxu Fan ,&nbsp;Xiaomei Yu ,&nbsp;Xue Li ,&nbsp;Fengru Ge ,&nbsp;Yanjie Zhao","doi":"10.1016/j.jksuci.2024.102245","DOIUrl":"10.1016/j.jksuci.2024.102245","url":null,"abstract":"<div><div>The rapid accumulation of large-scale electronic health records (EHRs) has witnessed the prosperity of intelligent medicine, such as medication recommendation (MR). However, most studies either fail to fully capture the structural correlation and temporal dependence among various medical records, or disregard the computational efficiency of the MR models. To fill this gap, we put forward a <strong>L</strong>ightweight <strong>M</strong>edication recommendation method which integrates bidirectional gate recurrent units (BiGRUs) with light graph convolutional networks (LGCNs) based on the multiple <strong>G</strong>raph <strong>A</strong>ugmentation networks (LMGA). Specifically, BiGRUs are deployed to encode longitudinal visit histories and generate patient representations from a holistic perspective. Additionally, a memory network is constructed to extract local personalized features in the patients’ historical EHRs, and LGCNs are deployed to learn both drug co-occurrence and antagonistic relationships for integral drug representations with reduced computational resource requirements. Moreover, a drug molecular graph is leveraged to capture structural information and control potential DDIs in predicted medication combinations. Incorporating the representations of patients and medications, a lightweight and safe medication recommendation is available to promote prediction performance with reduced computational resource consumption. Finally, we conduct a series of experiments to evaluate the proposed LMGA on two publicly available datasets, and the experimental results demonstrate the superior performance of LMGA in MR tasks compared with the state-of-the-art baseline models.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102245"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705872","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}

{"title":"L2-MA-CPABE: A ciphertext access control scheme integrating blockchain and off-chain computation with zero knowledge proof","authors":"Zhixin Ren,&nbsp;Yimin Yu,&nbsp;Enhua Yan,&nbsp;Taowei Chen","doi":"10.1016/j.jksuci.2024.102247","DOIUrl":"10.1016/j.jksuci.2024.102247","url":null,"abstract":"<div><div>To enhance the security of ciphertext-policy attribute-based encryption (CP-ABE) and achieve fully distributed key generation (DKG), this paper proposes a ciphertext access control scheme integrating blockchain and off-chain computation with zero knowledge proof based on Layer-2 and multi-authority CP-ABE. Firstly, we enhance the system into two layers and construct a Layer-2 distributed key management service framework. This framework improves system efficiency and scalability while reducing costs. Secondly, we design the proof of trust contribution (PoTC) consensus algorithm to elect high-trust nodes responsible for DKG and implement an incentive mechanism for key computation through smart contract design. Finally, we design a non-interactive zero-knowledge proof protocol to achieve correctness verification of off-chain key computation. Security analysis and simulation experiments demonstrate that our scheme achieves high security while significantly improving system performance. The time consumption for data users to obtain attribute private keys is controlled at tens of milliseconds.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102247"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705877","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}

{"title":"Enhancing Internet of Things communications: Development of a new S-box and multi-layer encryption framework","authors":"Adel R. Alharbi ,&nbsp;Amer Aljaedi ,&nbsp;Abdullah Aljuhni ,&nbsp;Moahd K. Alghuson ,&nbsp;Hussain Aldawood ,&nbsp;Sajjad Shaukat Jamal ,&nbsp;Tariq Shah","doi":"10.1016/j.jksuci.2024.102265","DOIUrl":"10.1016/j.jksuci.2024.102265","url":null,"abstract":"<div><div>The growth of IoT applications has revolutionized sectors like security and home automation but raised concerns about data breaches due to device limitations. This research proposes a novel substitution box and cryptographic scheme designed to secure data transmission in IoT devices like smartphones and smartwatches. The proposed research has two phases: (I) generation of a substitution box (S-box) which is proposed by dividing phase space into 256 regions (0–255) using a random initial value and control parameter for the Piecewise Linear Chaotic Map (PWLCM), iterated multiple times, and (ii) a new encryption scheme, which is proposed by employing advanced cryptographic techniques such as bit-plane extraction, diffusion, and a three-stage scrambling process (multiround, multilayer, and recursive). Scrambled data is substituted using multiple S-boxes, followed by XOR operations with random image bit-planes to generate pre-ciphertext. Finally, quantum encryption operations, including Hadamard, CNOT, and phase gates, are applied to produce the fully encrypted image. The research evaluates the robustness of the proposed S-box and encryption scheme through experimental analyses, including nonlinearity, strict avalanche criterion (SAC), linear approximation probability (LAP), bit independence criterion (BIC), key space, entropy, correlation, energy, and histogram variance. The proposed approach demonstrates an impressive statistical performance with key metrics such as nonlinearity of 108.75, SAC of 0.5010, LAP of 0.0903, BIC of 110.65, a key space exceeding <span><math><msup><mrow><mn>2</mn></mrow><mrow><mn>100</mn></mrow></msup></math></span>, entropy of 7.9998, correlation of 0.0001, and energy of 0.0157. Furthermore, the proposed encryption scheme can encrypt a plaintext image of size 256 × 256 within one second which demonstrates its suitability for IoT devices that require fast computation.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 10","pages":"Article 102265"},"PeriodicalIF":5.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180401","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}

{"title":"IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection","authors":"Jiahao Li ,&nbsp;Tao Luo,&nbsp;Baitao Zhang,&nbsp;Min Chen,&nbsp;Jie Zhou","doi":"10.1016/j.jksuci.2024.102205","DOIUrl":"10.1016/j.jksuci.2024.102205","url":null,"abstract":"<div><div>With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.</div></div>","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102205"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432506","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}

{"title":"Framework to improve software effort estimation accuracy using novel ensemble rule","authors":"Syed Sarmad Ali ,&nbsp;Jian Ren ,&nbsp;Ji Wu","doi":"10.1016/j.jksuci.2024.102189","DOIUrl":"10.1016/j.jksuci.2024.102189","url":null,"abstract":"&lt;div&gt;&lt;div&gt;This investigation focuses on refining software effort estimation (SEE) to enhance project outcomes amidst the rapid evolution of the software industry. Accurate estimation is a cornerstone of project success, crucial for avoiding budget overruns and minimizing the risk of project failures. The framework proposed in this article addresses three significant issues that are critical for accurate estimation: dealing with missing or inadequate data, selecting key features, and improving the software effort model. Our proposed framework incorporates three methods: the &lt;em&gt;Novel Incomplete Value Imputation Model (NIVIM)&lt;/em&gt;, a hybrid model using &lt;em&gt;Correlation-based Feature Selection with a meta-heuristic algorithm (CFS-Meta)&lt;/em&gt;, and the &lt;em&gt;Heterogeneous Ensemble Model (HEM)&lt;/em&gt;. The combined framework synergistically enhances the robustness and accuracy of SEE by effectively handling missing data, optimizing feature selection, and integrating diverse predictive models for superior performance across varying project scenarios. The framework significantly reduces imputation and feature selection overhead, while the ensemble approach optimizes model performance through dynamic weighting and meta-learning. This results in lower mean absolute error (MAE) and reduced computational complexity, making it more effective for diverse software datasets. NIVIM is engineered to address incomplete datasets prevalent in SEE. By integrating a synthetic data methodology through a Variational Auto-Encoder (VAE), the model incorporates both contextual relevance and intrinsic project features, significantly enhancing estimation precision. Comparative analyses reveal that NIVIM surpasses existing models such as VAE, GAIN, K-NN, and MICE, achieving statistically significant improvements across six benchmark datasets, with average RMSE improvements ranging from &lt;em&gt;11.05%&lt;/em&gt; to &lt;em&gt;17.72%&lt;/em&gt; and MAE improvements from &lt;em&gt;9.62%&lt;/em&gt; to &lt;em&gt;21.96%&lt;/em&gt;. Our proposed method, CFS-Meta, balances global optimization with local search techniques, substantially enhancing predictive capabilities. The proposed CFS-Meta model was compared to single and hybrid feature selection models to assess its efficiency, demonstrating up to a &lt;em&gt;25.61%&lt;/em&gt; reduction in MSE. Additionally, the proposed CFS-Meta achieves a &lt;em&gt;10%&lt;/em&gt; (MAE) improvement against the hybrid PSO-SA model, an &lt;em&gt;11.38%&lt;/em&gt; (MAE) improvement compared to the Hybrid ABC-SA model, and &lt;em&gt;12.42%&lt;/em&gt; and &lt;em&gt;12.703%&lt;/em&gt; (MAE) improvements compared to the hybrid Tabu-GA and hybrid ACO-COA models, respectively. Our third method proposes an ensemble effort estimation (EEE) model that amalgamates diverse standalone models through a Dynamic Weight Adjustment-stacked combination (DWSC) rule. Tested against international benchmarks and industry datasets, the HEM method has improved the standalone model by an average of &lt;em&gt;21.8%&lt;/em&gt; (Pred()) and the homogeneous ensemble model by &lt;em&gt;15%&lt;/em&gt; (Pred()). This","PeriodicalId":48547,"journal":{"name":"Journal of King Saud University-Computer and Information Sciences","volume":"36 9","pages":"Article 102189"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424438","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}

{"title":"ParaU-Net: An improved UNet parallel coding network for lung nodule segmentation","authors":"Yingqi Lu ,&nbsp;Xiangsuo Fan ,&nbsp;Jinfeng Wang ,&nbsp;Shaojun Chen ,&nbsp;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":"2024-11-01","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}

{"title":"Fast and robust JND-guided video watermarking scheme in spatial domain","authors":"Antonio Cedillo-Hernandez ,&nbsp;Lydia Velazquez-Garcia ,&nbsp;Manuel Cedillo-Hernandez ,&nbsp;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":"2024-11-01","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}