Neural Computing & Applications最新文献

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Real-time automated detection of older adults' hand gestures in home and clinical settings. 实时自动检测老年人的手势在家庭和临床设置。
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-08090-8
Guan Huang, Son N Tran, Quan Bai, Jane Alty
{"title":"Real-time automated detection of older adults' hand gestures in home and clinical settings.","authors":"Guan Huang,&nbsp;Son N Tran,&nbsp;Quan Bai,&nbsp;Jane Alty","doi":"10.1007/s00521-022-08090-8","DOIUrl":"https://doi.org/10.1007/s00521-022-08090-8","url":null,"abstract":"<p><p>There is an urgent need, accelerated by the COVID-19 pandemic, for methods that allow clinicians and neuroscientists to remotely evaluate hand movements. This would help detect and monitor degenerative brain disorders that are particularly prevalent in older adults. With the wide accessibility of computer cameras, a vision-based real-time hand gesture detection method would facilitate online assessments in home and clinical settings. However, motion blur is one of the most challenging problems in the fast-moving hands data collection. The objective of this study was to develop a computer vision-based method that accurately detects older adults' hand gestures using video data collected in real-life settings. We invited adults over 50 years old to complete validated hand movement tests (fast finger tapping and hand opening-closing) at home or in clinic. Data were collected without researcher supervision via a website programme using standard laptop and desktop cameras. We processed and labelled images, split the data into training, validation and testing, respectively, and then analysed how well different network structures detected hand gestures. We recruited 1,900 adults (age range 50-90 years) as part of the TAS Test project and developed UTAS7k-a new dataset of 7071 hand gesture images, split 4:1 into clear: motion-blurred images. Our new network, RGRNet, achieved 0.782 mean average precision (mAP) on clear images, outperforming the state-of-the-art network structure (YOLOV5-P6, mAP 0.776), and mAP 0.771 on blurred images. A new robust real-time automated network that detects static gestures from a single camera, RGRNet, and a new database comprising the largest range of individual hands, UTAS7k, both show strong potential for medical and research applications.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s00521-022-08090-8.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 11","pages":"8143-8156"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741488/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9212557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 1
Evaluating deep learning predictions for COVID-19 from X-ray images using leave-one-out predictive densities. 利用留一预测密度评估x射线图像对COVID-19的深度学习预测。
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-023-08219-3
Sergio Hernández, Xaviera López-Córtes
{"title":"Evaluating deep learning predictions for COVID-19 from X-ray images using leave-one-out predictive densities.","authors":"Sergio Hernández,&nbsp;Xaviera López-Córtes","doi":"10.1007/s00521-023-08219-3","DOIUrl":"https://doi.org/10.1007/s00521-023-08219-3","url":null,"abstract":"<p><p>Early detection of the COVID-19 virus is an important task for controlling the spread of the pandemic. Imaging techniques such as chest X-ray are relatively inexpensive and accessible, but its interpretation requires expert knowledge to evaluate the disease severity. Several approaches for automatic COVID-19 detection using deep learning techniques have been proposed. While most approaches show high accuracy on the COVID-19 detection task, there is not enough evidence on external evaluation for this technique. Furthermore, data scarcity and sampling biases make difficult to properly evaluate model predictions. In this paper, we propose stochastic gradient Langevin dynamics (SGLD) to take into account the model uncertainty. Four different deep learning architectures are trained using SGLD and compared to their baselines using stochastic gradient descent. The model uncertainties are also evaluated according to their convergence properties and the leave-one-out predictive densities. The proposed approach is able to reduce overconfidence of the baseline estimators while also retaining predictive accuracy for the best-performing cases.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 13","pages":"9819-9830"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9900537/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9330144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 2
Robust thermal infrared tracking via an adaptively multi-feature fusion model. 通过自适应多特征融合模型实现稳健的热红外跟踪。
IF 4.5 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2022-10-12 DOI: 10.1007/s00521-022-07867-1
Di Yuan, Xiu Shu, Qiao Liu, Xinming Zhang, Zhenyu He
{"title":"Robust thermal infrared tracking via an adaptively multi-feature fusion model.","authors":"Di Yuan, Xiu Shu, Qiao Liu, Xinming Zhang, Zhenyu He","doi":"10.1007/s00521-022-07867-1","DOIUrl":"10.1007/s00521-022-07867-1","url":null,"abstract":"<p><p>When dealing with complex thermal infrared (TIR) tracking scenarios, the single category feature is not sufficient to portray the appearance of the target, which drastically affects the accuracy of the TIR target tracking method. In order to address these problems, we propose an adaptively multi-feature fusion model (AMFT) for the TIR tracking task. Specifically, our AMFT tracking method adaptively integrates hand-crafted features and deep convolutional neural network (CNN) features. In order to accurately locate the target position, it takes advantage of the complementarity between different features. Additionally, the model is updated using a simple but effective model update strategy to adapt to changes in the target during tracking. In addition, a simple but effective model update strategy is adopted to adapt the model to the changes of the target during the tracking process. We have shown through ablation studies that the adaptively multi-feature fusion model in our AMFT tracking method is very effective. Our AMFT tracker performs favorably on PTB-TIR and LSOTB-TIR benchmarks compared with state-of-the-art trackers.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 4","pages":"3423-3434"},"PeriodicalIF":4.5,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10630224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment. 球形模糊环境下基于SWARA和MARCOS的道路安全评价与风险排序
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07929-4
Saeid Jafarzadeh Ghoushchi, Sina Shaffiee Haghshenas, Ali Memarpour Ghiaci, Giuseppe Guido, Alessandro Vitale
{"title":"Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment.","authors":"Saeid Jafarzadeh Ghoushchi,&nbsp;Sina Shaffiee Haghshenas,&nbsp;Ali Memarpour Ghiaci,&nbsp;Giuseppe Guido,&nbsp;Alessandro Vitale","doi":"10.1007/s00521-022-07929-4","DOIUrl":"https://doi.org/10.1007/s00521-022-07929-4","url":null,"abstract":"<p><p>There are a lot of elements that make road safety assessment situations unpredictable and hard to understand. This could put people's lives in danger, hurt the mental health of a society, and cause permanent financial and human losses. Due to the ambiguity and uncertainty of the risk assessment process, a multi-criteria decision-making technique for dealing with complex systems that involves choosing one of many options is an important strategy of assessing road safety. In this study, an integrated stepwise weight assessment ratio analysis (SWARA) with measurement of alternatives and ranking according to compromise solution (MARCOS) approach under a spherical fuzzy (SF) set was considered. Then, the proposed methodology was applied to develop the approach of failure mode and effect analysis (FMEA) for rural roads in Cosenza, southern Italy. Also, the results of modified FMEA by SF-SWARA-MARCOS were compared with the results of conventional FMEA. The risk score results demonstrated that the source of risk (human) plays a significant role in crashes compared to other sources of risk. The two risks, including landslides and floods, had the lowest values among the factors affecting rural road safety in Calabria, respectively. The correlation between scenario outcomes and main ranking orders in weight values was also investigated. This study was done in line with the goals of sustainable development and the goal of sustainable mobility, which was to find risks and lower the number of accidents on the road. As a result, it is thus essential to reconsider laws and measures necessary to reduce human risks on the regional road network of Calabria to improve road safety.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 6","pages":"4549-4567"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9595097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10690821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 27
DSmishSMS-A System to Detect Smishing SMS. dsmishsms -一个检测诈骗短信的系统。
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-021-06305-y
Sandhya Mishra, Devpriya Soni
{"title":"DSmishSMS-A System to Detect Smishing SMS.","authors":"Sandhya Mishra,&nbsp;Devpriya Soni","doi":"10.1007/s00521-021-06305-y","DOIUrl":"https://doi.org/10.1007/s00521-021-06305-y","url":null,"abstract":"<p><p>With the origin of smart homes, smart cities, and smart everything, smart phones came up as an area of magnificent growth and development. These devices became a part of daily activities of human life. This impact and growth have made these devices more vulnerable to attacks than other devices such as desktops or laptops. Text messages or SMS (Short Text Messages) are a part of smartphones through which attackers target the users. Smishing (SMS Phishing) is an attack targeting smartphone users through the medium of text messages. Though smishing is a type of phishing, it is different from phishing in many aspects like the amount of information available in the SMS, the strategy of attack, etc. Thus, detection of smishing is a challenge in the context of the minimum amount of information shared by the attacker. In the case of smishing, we have short text messages which are often in short forms or in symbolic forms. A single text message contains very few smishing-related features, and it consists of abbreviations and idioms which makes smishing detection more difficult. Detection of smishing is a challenge not only because of features constraint but also due to the scarcity of real smishing datasets. To differentiate spam messages from smishing messages, we are evaluating the legitimacy of the URL (Uniform Resource Locator) in the message. We have extracted the five most efficient features from the text messages to enable the machine learning classification using a limited number of features. In this paper, we have presented a smishing detection model comprising of two phases, Domain Checking Phase and SMS Classification Phase. We have examined the authenticity of the URL in the SMS which is a crucial part of SMS phishing detection. In our system, Domain Checking Phase scrutinizes the authenticity of the URL. SMS Classification Phase examines the text contents of the messages and extracts some efficient features. Finally, the system classifies the messages using Backpropagation Algorithm and compares results with three traditional classifiers. A prototype of the system has been developed and evaluated using SMS datasets. The results of the evaluation achieved an accuracy of 97.93% which shows the proposed method is very efficient for the detection of smishing messages.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 7","pages":"4975-4992"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s00521-021-06305-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10691025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 16
Framework for detection of probable clues to predict misleading information proliferated during COVID-19 outbreak. 在COVID-19疫情期间,发现可能线索以预测误导性信息的框架激增。
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07938-3
Deepika Varshney, Dinesh Kumar Vishwakarma
{"title":"Framework for detection of probable clues to predict misleading information proliferated during COVID-19 outbreak.","authors":"Deepika Varshney,&nbsp;Dinesh Kumar Vishwakarma","doi":"10.1007/s00521-022-07938-3","DOIUrl":"https://doi.org/10.1007/s00521-022-07938-3","url":null,"abstract":"<p><p>Spreading of misleading information on social web platforms has fuelled huge panic and confusion among the public regarding the Corona disease, the detection of which is of paramount importance. To identify the credibility of the posted claim, we have analyzed possible evidence from the news articles in the google search results. This paper proposes an intelligent and expert strategy to gather important clues from the top 10 google search results related to the claim. The N-gram, Levenshtein Distance, and Word-Similarity-based features are used to identify the clues from the news article that can automatically warn users against spreading false news if no significant supportive clues are identified concerning that claim. The complete process is done in four steps, wherein the first step we build a query from the posted claim received in the form of text or text additive images which further goes as an input to the search query phase, where the top 10 google results are processed. In the third step, the important clues are extracted from titles of the top 10 news articles. Lastly, useful pieces of evidence are extracted from the content of each news article. All the useful clues with respect to N-gram, Levenshtein Distance, and Word Similarity are finally fed into the machine learning model for classification and to evaluate its performances. It has been observed that our proposed intelligent strategy gives promising experimental results and is quite effective in predicting misleading information. The proposed work provides practical implications for the policymakers and health practitioners that could be useful in protecting the world from misleading information proliferation during this pandemic.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 8","pages":"5999-6013"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9660173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10801914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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
Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety. 静息状态神经振荡功率与特质焦虑之间的空间模式识别。
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07847-5
Carmen Vidaurre, Vadim V Nikulin, Maria Herrojo Ruiz
{"title":"Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety.","authors":"Carmen Vidaurre,&nbsp;Vadim V Nikulin,&nbsp;Maria Herrojo Ruiz","doi":"10.1007/s00521-022-07847-5","DOIUrl":"https://doi.org/10.1007/s00521-022-07847-5","url":null,"abstract":"<p><p>Anxiety affects approximately 5-10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets. In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4-7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8-12 Hz) or beta (13-30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus. We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 8","pages":"5737-5749"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9525925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10820887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 3
Communicative capital: a key resource for human-machine shared agency and collaborative capacity. 交流资本:人机共享代理和协作能力的关键资源。
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 Epub Date: 2022-11-14 DOI: 10.1007/s00521-022-07948-1
Kory W Mathewson, Adam S R Parker, Craig Sherstan, Ann L Edwards, Richard S Sutton, Patrick M Pilarski
{"title":"Communicative capital: a key resource for human-machine shared agency and collaborative capacity.","authors":"Kory W Mathewson,&nbsp;Adam S R Parker,&nbsp;Craig Sherstan,&nbsp;Ann L Edwards,&nbsp;Richard S Sutton,&nbsp;Patrick M Pilarski","doi":"10.1007/s00521-022-07948-1","DOIUrl":"10.1007/s00521-022-07948-1","url":null,"abstract":"<p><p>In this work, we present a perspective on the role machine intelligence can play in supporting human abilities. In particular, we consider research in rehabilitation technologies such as prosthetic devices, as this domain requires tight coupling between human and machine. Taking an agent-based view of such devices, we propose that human-machine collaborations have a capacity to perform tasks which is a result of the combined agency of the human and the machine. We introduce <i>communicative capital</i> as a resource developed by a human and a machine working together in ongoing interactions. Development of this resource enables the partnership to eventually perform tasks at a capacity greater than either individual could achieve alone. We then examine the benefits and challenges of increasing the agency of prostheses by surveying literature which demonstrates that building communicative resources enables more complex, task-directed interactions. The viewpoint developed in this article extends current thinking on how best to support the functional use of increasingly complex prostheses, and establishes insight toward creating more fruitful interactions between humans and supportive, assistive, and augmentative technologies.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 23","pages":"16805-16819"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338399/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10201124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 2
Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model. 基于浅神经网络和深度神经网络的时序医药数据需求预测模型。
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-022-07889-9
R Rathipriya, Abdul Aziz Abdul Rahman, S Dhamodharavadhani, Abdelrhman Meero, G Yoganandan
{"title":"Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model.","authors":"R Rathipriya,&nbsp;Abdul Aziz Abdul Rahman,&nbsp;S Dhamodharavadhani,&nbsp;Abdelrhman Meero,&nbsp;G Yoganandan","doi":"10.1007/s00521-022-07889-9","DOIUrl":"https://doi.org/10.1007/s00521-022-07889-9","url":null,"abstract":"<p><p>Demand forecasting is a scientific and methodical assessment of future demand for a critical product.The effective Demand Forecast Model (DFM) enables pharmaceutical companies to be successful in the global market. The purpose of this research paper is to validate various shallow and deep neural network methods for demand forecasting, with the aim of recommending sales and marketing strategies based on the trend/seasonal effects of eight different groups of pharmaceutical products with different characteristics. The root mean squared error (RMSE) is used as the predictive accuracy of DFMs. This study also found that the mean RMSE value of the shallow neural network-based DFMs was 6.27 for all drug categories, which was lower than deep neural network models. According to the findings, DFMs based on shallow neural networks can effectively estimate future demand for pharmaceutical products.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 2","pages":"1945-1957"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9540101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10517070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 5
DCU-Net: a dual-channel U-shaped network for image splicing forgery detection. DCU-Net:用于图像拼接伪造检测的双通道u型网络。
IF 6 3区 计算机科学
Neural Computing & Applications Pub Date : 2023-01-01 DOI: 10.1007/s00521-021-06329-4
Hongwei Ding, Leiyang Chen, Qi Tao, Zhongwang Fu, Liang Dong, Xiaohui Cui
{"title":"DCU-Net: a dual-channel U-shaped network for image splicing forgery detection.","authors":"Hongwei Ding,&nbsp;Leiyang Chen,&nbsp;Qi Tao,&nbsp;Zhongwang Fu,&nbsp;Liang Dong,&nbsp;Xiaohui Cui","doi":"10.1007/s00521-021-06329-4","DOIUrl":"https://doi.org/10.1007/s00521-021-06329-4","url":null,"abstract":"<p><p>The detection and location of image splicing forgery are a challenging task in the field of image forensics. It is to study whether an image contains a suspicious tampered area pasted from another image. In this paper, we propose a new image tamper location method based on dual-channel U-Net, that is, DCU-Net. The detection framework based on DCU-Net is mainly divided into three parts: encoder, feature fusion, and decoder. Firstly, high-pass filters are used to extract the residual of the tampered image and generate the residual image, which contains the edge information of the tampered area. Secondly, a dual-channel encoding network model is constructed. The input of the model is the original tampered image and the tampered residual image. Then, the deep features extracted from the dual-channel encoding network are fused for the first time, and then the tampered features with different granularity are extracted by dilation convolution, and then, the secondary fusion is carried out. Finally, the fused feature map is input into the decoder, and the predicted image is decoded layer by layer. The experimental results on Casia2.0 and Columbia datasets show that DCU-Net performs better than the latest algorithm and can accurately locate tampered areas. In addition, the attack experiments show that DCU-Net model has good robustness and can resist noise and JPEG recompression attacks.</p>","PeriodicalId":49766,"journal":{"name":"Neural Computing & Applications","volume":"35 7","pages":"5015-5031"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s00521-021-06329-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10275922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 17
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