To address the high-quality forged videos,traditional approaches typically have low recognition accuracy and tend to be easily misclassified.This paper tries to address the challenge of detecting high-quality deepfake...To address the high-quality forged videos,traditional approaches typically have low recognition accuracy and tend to be easily misclassified.This paper tries to address the challenge of detecting high-quality deepfake videos by promoting the accuracy of Artificial Intelligence Generated Content(AIGC)video authenticity detection with a multimodal information fusion approach.First,a high-quality multimodal video dataset is collected and normalized,including resolution correction and frame rate unification.Next,feature extraction techniques are employed to draw out features from visual,audio,and text modalities.Subsequently,these features are fused into a multilayer perceptron and attention mechanisms-based multimodal feature matrix.Finally,the matrix is fed into a multimodal information fusion layer in order to construct and train a deep learning model.Experimental findings show that the multimodal fusion model achieves an accuracy of 93.8%for the detection of video authenticity,showing significant improvement against other unimodal models,as well as affirming better performance and resistance of the model to AIGC video authenticity detection.展开更多
Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism litera...Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism literature lacks empirical evidence of the tourism network in lessdeveloped mountainous regions where the development of transport infrastructure is more variable.This paper aims to provide such evidence using Guangxi Zhuang Autonomous Region in China as a case study.Using User Generated Content(UGC)data,this study constructs a tourism network in Guangxi.By integrating social network analysis with spatial interaction modelling,we compared the impact of two different transport infrastructures,highway and high-speed railway,on tourist flows,particularly in less-developed mountainous regions.It was found that the product of node centrality and flow could best describe the significant pushing and pulling forces on the flow of tourists.The tourism by high-speed railway was sensitive to the position of trip destination on the whole tourism network but self-drive tourism was more sensitive to travelling time.The increase of high-speed railway density is crucial to promote local tourism-led economic development,however,large-scale karst landforms in the study area present a significant obstacle to the construction of high-speed railways.展开更多
In recent years,computing art has developed rapidly with the in-depth cross study of artificial intelligence generated con-tent(AIGC)and the main features of artworks.Audio-visual content generation has gradually been...In recent years,computing art has developed rapidly with the in-depth cross study of artificial intelligence generated con-tent(AIGC)and the main features of artworks.Audio-visual content generation has gradually been applied to various practical tasks,including video or game score,assisting artists in creation,art education and other aspects,which demonstrates a broad application pro-spect.In this paper,we introduce innovative achievements in audio-visual content generation from the perspective of visual art genera-tion and auditory art generation based on artificial intelligence(Al).We outline the development tendency of image and music datasets,visual and auditory content modelling,and related automatic generation systems.The objective and subjective evaluation of generated samples plays an important role in the measurement of algorithm performance.We provide a cogeneration mechanism of audio-visual content in multimodal tasks from image to music and display the construction of specific stylized datasets.There are still many new op-portunities and challenges in the field of audio-visual synesthesia generation,and we provide a comprehensive discussion on them.展开更多
In the context of rapid population aging and ongoing digital transformation,cultural and entertainment services have become an essential component of quality-of-life enhancement for elderly populations.Generative arti...In the context of rapid population aging and ongoing digital transformation,cultural and entertainment services have become an essential component of quality-of-life enhancement for elderly populations.Generative artifcial intelligence(AI),with its capacity for multimodal content creation and adaptive personalization,offers new possibilities for addressing the diversifed cultural needs of older adults.This study explores the application of generative AI in elderly cultural and entertainment content from the perspectives of content generation,creative assistance,immersive experience design,and service delivery.Drawing on a practice-oriented and user-centered methodological framework,the research integrates qualitative needs assessment,AI-assisted content production,and iterative evaluation based on elderly user feedback.The fndings suggest that generative AI can effectively support the transition from standardized cultural provision toward adaptive,interactive,and culturally sensitive service models.By situating technological innovation within a human-centered and culturally grounded research context,this study provides practical insights for the development of intelligent cultural services in aging societies.展开更多
In Web 2.0 era,the content on a web page is increasingly generated by end users,rather than limited number of administrators.Hence,large number of User Generated Content(UGC) has driven the explosion of content in the...In Web 2.0 era,the content on a web page is increasingly generated by end users,rather than limited number of administrators.Hence,large number of User Generated Content(UGC) has driven the explosion of content in the web.Thanks to UGC,the pattern of web usage has evolved from download dominated way to a hybrid one with both information download and upload.Large number of UGC has unveiled great capacity of information that is unavailable for researchers before,such as individual preferences,social connections,and etc.In this paper,we propose a novel model which studies the UGC in micro-blogging web sites,the largest and fastest information diffusion media online,and evaluate the social influence for an arbitrary individual.Experimental results show that our model outperforms state-of-the-art techniques in social influence evaluation in both the running time and accuracy.展开更多
Purpose:Currently,social tagging behavior,including social tag,online review and score information,has been investigated extensively,however,there are very few works about the relationship among them.In this paper,we ...Purpose:Currently,social tagging behavior,including social tag,online review and score information,has been investigated extensively,however,there are very few works about the relationship among them.In this paper,we have investigated the problem using Douban Website as the research object.Design/methodology/approach:Firstly,we divided social tags into those with high and low frequency counts,respectively,divided books into popular and unpopular books according to books’popularity,and chose core tags in terms of distribution;Secondly,we conducted an investigation on the relationship between social tags and books scores including comprehensive analyses and assorted analyses.Findings:The more popular the books become,the higher scores they will get.Tag frequency is not related with book scores directly,and neither does the tag distribution weight.Tags in books of'fashion'category are relatively disordered,which may associate with books miscellany and readers diversity.Research limitations:Social tags are growing dramatically,strategies and researches to this respect are just experimental exploration.Open source books,data and educational resources are not consummate.Comparative studies are necessary,but the result may be affected by researches based on data analyses.In addition,this research has been conducted only on one website,namely Douban,and the tags provided by Douban Book are not complete.All these factors could influence the versatility of the results.Practical implications:There are very a few studies that have been conducted on the relationship between tags and scores,and this research could bring a certain practical significance to popular books prediction and tags’quality research.Originality/value:Less attention has been paid to Chinese books while analyzing relationship between scores and tags of user generated content.Analyses based on the Chinese books may fill in the gap of better understanding the relationship between the two objects.展开更多
This book comprehensively expounds the basic concepts of AIGC(Artificial Intelligence Generated Content),and helps readers deeply understand the application of AIGC in various scenarios through examples and operation ...This book comprehensively expounds the basic concepts of AIGC(Artificial Intelligence Generated Content),and helps readers deeply understand the application of AIGC in various scenarios through examples and operation guides.The book consists of seven chapters,including an overview and fundamentals of generative Al,natural language generation of creative content,image processing and generation,and so on.展开更多
"Today,AIGC(Artifcial Intelligence Generated Content)is capable of aeating fakes aross multiple modalities,indudingvideo,audio,text and even miaro-expression synthesis.As AI becomes involved in the production of ..."Today,AIGC(Artifcial Intelligence Generated Content)is capable of aeating fakes aross multiple modalities,indudingvideo,audio,text and even miaro-expression synthesis.As AI becomes involved in the production of false information and becomes a profitdriven industry,the scale effect will ause the difculty and cost of finding accurate information to rise exponentially for ordinary users."展开更多
In November 2022,the emergence of ChatGPT brought unprecedented attention to AIGC(Artificial Intelligence Generated Content).OpenATs ChatGPT sparked a global sensation;Google followed closely with Bard;and Baidu launc...In November 2022,the emergence of ChatGPT brought unprecedented attention to AIGC(Artificial Intelligence Generated Content).OpenATs ChatGPT sparked a global sensation;Google followed closely with Bard;and Baidu launched its“domestic version of ChatGPT,”ERNIE Bot(also known as Wenxin Yiyan).In fact,even before the birth of ChatGPT,AIGC technology had already been widely applied,driving a new revolution in content production and bringing about deep changes in the media ecosystem.Everyone is a media producer,everything is media,and the flow of information is like rivers converging,expanding the boundaries of media into every comer.As the watchful eyes of the era,mainstream media stands at the forefront of the digital wave,facing unprecedented challenges.展开更多
Dear Editor,In recent years,generative artificial intelligence(GAI)has gained unprecedented attention.Unlike conventional AI,GAI can generate innovative and meaningful content across texts,images,and videos.The succes...Dear Editor,In recent years,generative artificial intelligence(GAI)has gained unprecedented attention.Unlike conventional AI,GAI can generate innovative and meaningful content across texts,images,and videos.The success of OpenAI’s ChatGPT has driven global tech companies to develop high-performancemodels and integrate GAI into products.1 This AI arms race continues,as shown by OpenAI’s textto-video model,Sora,and Anthropic’s new large language model,Claude 3.The release of DeepSeek V3/R1 has sparked a global AI cost revolution.GAI is transforming key sectors like business,finance,law,and healthcare,heralding a new era in AI technology.展开更多
Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, p...Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.展开更多
Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intel...Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intelligence,generative AI,as exemplified by ChatGPT,exhibits characteristics that increasingly resemble human-level comprehension and creation processes.This paper provides a detailed technical framework and history of ChatGPT,followed by an examination of the challenges posed to political security,military security,economic security,cultural security,social security,ethical security,legal security,machine escape problems,and information leakage.Finally,this paper discusses the potential opportunities that AIGC presents in the realms of politics,military,cybersecurity,society,and public safety education.展开更多
文摘To address the high-quality forged videos,traditional approaches typically have low recognition accuracy and tend to be easily misclassified.This paper tries to address the challenge of detecting high-quality deepfake videos by promoting the accuracy of Artificial Intelligence Generated Content(AIGC)video authenticity detection with a multimodal information fusion approach.First,a high-quality multimodal video dataset is collected and normalized,including resolution correction and frame rate unification.Next,feature extraction techniques are employed to draw out features from visual,audio,and text modalities.Subsequently,these features are fused into a multilayer perceptron and attention mechanisms-based multimodal feature matrix.Finally,the matrix is fed into a multimodal information fusion layer in order to construct and train a deep learning model.Experimental findings show that the multimodal fusion model achieves an accuracy of 93.8%for the detection of video authenticity,showing significant improvement against other unimodal models,as well as affirming better performance and resistance of the model to AIGC video authenticity detection.
基金funded by the Guangxi Natural Science Foundation(Grant No.2020GXNSFAA159065)the Opening Fund of Key Laboratory of Environment Change and Resources Use in Beibu Gulf under Ministry of Education(Nanning Normal University)+1 种基金Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation(Nanning Normal University)(Grant No.GTEU-KLOP-K1701)the seventh batch of distinguished experts in Guangxi and National Natural Science Foundation of China(Grant No.41867071)。
文摘Tourism-led economic growth and tourism-driven urbanization have attracted increasing attention by provinces and regions in China with abundant tourism resources.Due to low data availability,the current tourism literature lacks empirical evidence of the tourism network in lessdeveloped mountainous regions where the development of transport infrastructure is more variable.This paper aims to provide such evidence using Guangxi Zhuang Autonomous Region in China as a case study.Using User Generated Content(UGC)data,this study constructs a tourism network in Guangxi.By integrating social network analysis with spatial interaction modelling,we compared the impact of two different transport infrastructures,highway and high-speed railway,on tourist flows,particularly in less-developed mountainous regions.It was found that the product of node centrality and flow could best describe the significant pushing and pulling forces on the flow of tourists.The tourism by high-speed railway was sensitive to the position of trip destination on the whole tourism network but self-drive tourism was more sensitive to travelling time.The increase of high-speed railway density is crucial to promote local tourism-led economic development,however,large-scale karst landforms in the study area present a significant obstacle to the construction of high-speed railways.
基金This work was supported by National Natural Science Foundation of China(No.62176006)the National Key Research and Development Program of China(No.2022YFF0902302).
文摘In recent years,computing art has developed rapidly with the in-depth cross study of artificial intelligence generated con-tent(AIGC)and the main features of artworks.Audio-visual content generation has gradually been applied to various practical tasks,including video or game score,assisting artists in creation,art education and other aspects,which demonstrates a broad application pro-spect.In this paper,we introduce innovative achievements in audio-visual content generation from the perspective of visual art genera-tion and auditory art generation based on artificial intelligence(Al).We outline the development tendency of image and music datasets,visual and auditory content modelling,and related automatic generation systems.The objective and subjective evaluation of generated samples plays an important role in the measurement of algorithm performance.We provide a cogeneration mechanism of audio-visual content in multimodal tasks from image to music and display the construction of specific stylized datasets.There are still many new op-portunities and challenges in the field of audio-visual synesthesia generation,and we provide a comprehensive discussion on them.
基金supported by the 2021 Youth Project of Humanities and Social Sciences of the Ministry of Education,titled“Research on the Accessibility Design of Smart Home Elderly Care Service Information from the Perspective of Service Design”(21YJC760019)the 2024 Sichuan Normal University School-level Project of Talent Cultivation Quality and Teaching Reform,titled“Research on the Collaborative Training Mode of Art Design Application Talents between Schools and Enterprises in the Context of New Liberal Arts”(JWC20240505).
文摘In the context of rapid population aging and ongoing digital transformation,cultural and entertainment services have become an essential component of quality-of-life enhancement for elderly populations.Generative artifcial intelligence(AI),with its capacity for multimodal content creation and adaptive personalization,offers new possibilities for addressing the diversifed cultural needs of older adults.This study explores the application of generative AI in elderly cultural and entertainment content from the perspectives of content generation,creative assistance,immersive experience design,and service delivery.Drawing on a practice-oriented and user-centered methodological framework,the research integrates qualitative needs assessment,AI-assisted content production,and iterative evaluation based on elderly user feedback.The fndings suggest that generative AI can effectively support the transition from standardized cultural provision toward adaptive,interactive,and culturally sensitive service models.By situating technological innovation within a human-centered and culturally grounded research context,this study provides practical insights for the development of intelligent cultural services in aging societies.
基金ACKNOWLEDGEMENT This work was partially supported by the National Natural Science Foundation of China under Grants No. 61202179, No. 61173089 SRF for ROCS, SEM and the Fundamental Research Funds for the Central Universities.
文摘In Web 2.0 era,the content on a web page is increasingly generated by end users,rather than limited number of administrators.Hence,large number of User Generated Content(UGC) has driven the explosion of content in the web.Thanks to UGC,the pattern of web usage has evolved from download dominated way to a hybrid one with both information download and upload.Large number of UGC has unveiled great capacity of information that is unavailable for researchers before,such as individual preferences,social connections,and etc.In this paper,we propose a novel model which studies the UGC in micro-blogging web sites,the largest and fastest information diffusion media online,and evaluate the social influence for an arbitrary individual.Experimental results show that our model outperforms state-of-the-art techniques in social influence evaluation in both the running time and accuracy.
基金supported by the National Natural Science Foundation of China(Grant No.:71273126)the Foundation for Humanities and Social Science of the Chinese Ministry of Education(Grant No.:13YJA870020)
文摘Purpose:Currently,social tagging behavior,including social tag,online review and score information,has been investigated extensively,however,there are very few works about the relationship among them.In this paper,we have investigated the problem using Douban Website as the research object.Design/methodology/approach:Firstly,we divided social tags into those with high and low frequency counts,respectively,divided books into popular and unpopular books according to books’popularity,and chose core tags in terms of distribution;Secondly,we conducted an investigation on the relationship between social tags and books scores including comprehensive analyses and assorted analyses.Findings:The more popular the books become,the higher scores they will get.Tag frequency is not related with book scores directly,and neither does the tag distribution weight.Tags in books of'fashion'category are relatively disordered,which may associate with books miscellany and readers diversity.Research limitations:Social tags are growing dramatically,strategies and researches to this respect are just experimental exploration.Open source books,data and educational resources are not consummate.Comparative studies are necessary,but the result may be affected by researches based on data analyses.In addition,this research has been conducted only on one website,namely Douban,and the tags provided by Douban Book are not complete.All these factors could influence the versatility of the results.Practical implications:There are very a few studies that have been conducted on the relationship between tags and scores,and this research could bring a certain practical significance to popular books prediction and tags’quality research.Originality/value:Less attention has been paid to Chinese books while analyzing relationship between scores and tags of user generated content.Analyses based on the Chinese books may fill in the gap of better understanding the relationship between the two objects.
文摘This book comprehensively expounds the basic concepts of AIGC(Artificial Intelligence Generated Content),and helps readers deeply understand the application of AIGC in various scenarios through examples and operation guides.The book consists of seven chapters,including an overview and fundamentals of generative Al,natural language generation of creative content,image processing and generation,and so on.
文摘"Today,AIGC(Artifcial Intelligence Generated Content)is capable of aeating fakes aross multiple modalities,indudingvideo,audio,text and even miaro-expression synthesis.As AI becomes involved in the production of false information and becomes a profitdriven industry,the scale effect will ause the difculty and cost of finding accurate information to rise exponentially for ordinary users."
文摘In November 2022,the emergence of ChatGPT brought unprecedented attention to AIGC(Artificial Intelligence Generated Content).OpenATs ChatGPT sparked a global sensation;Google followed closely with Bard;and Baidu launched its“domestic version of ChatGPT,”ERNIE Bot(also known as Wenxin Yiyan).In fact,even before the birth of ChatGPT,AIGC technology had already been widely applied,driving a new revolution in content production and bringing about deep changes in the media ecosystem.Everyone is a media producer,everything is media,and the flow of information is like rivers converging,expanding the boundaries of media into every comer.As the watchful eyes of the era,mainstream media stands at the forefront of the digital wave,facing unprecedented challenges.
基金supported in part by the National Key Research and Development Program of China(No.2023YFE0119800)in part by the National Natural Science Foundation of China(No.72422015 and No.52277095).
文摘Dear Editor,In recent years,generative artificial intelligence(GAI)has gained unprecedented attention.Unlike conventional AI,GAI can generate innovative and meaningful content across texts,images,and videos.The success of OpenAI’s ChatGPT has driven global tech companies to develop high-performancemodels and integrate GAI into products.1 This AI arms race continues,as shown by OpenAI’s textto-video model,Sora,and Anthropic’s new large language model,Claude 3.The release of DeepSeek V3/R1 has sparked a global AI cost revolution.GAI is transforming key sectors like business,finance,law,and healthcare,heralding a new era in AI technology.
基金Project supported by the National Natural Science Foundation of China(Nos.62306075 and 62101136)the China Postdoctoral Science Foundation(No.2022TQ0069)+2 种基金the Natural Science Foundation of Shanghai,China(No.21ZR1403600)the Shanghai Municipal of Science and Technology Project,China(No.20JC1419500)the Shanghai Center for Brain Science and Brain-Inspired Technology,China。
文摘Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.
基金This work was supported by the National Science Foundation of China[NSFC41971366,4231476]Fundamental Research Funds for the Central Universities of China[buctrc202132].
文摘Artificial intelligence generated content(AIGC)is a production method based on artificial intelligence(AI)technology that finds rules through data and automatically generates content.In contrast to computational intelligence,generative AI,as exemplified by ChatGPT,exhibits characteristics that increasingly resemble human-level comprehension and creation processes.This paper provides a detailed technical framework and history of ChatGPT,followed by an examination of the challenges posed to political security,military security,economic security,cultural security,social security,ethical security,legal security,machine escape problems,and information leakage.Finally,this paper discusses the potential opportunities that AIGC presents in the realms of politics,military,cybersecurity,society,and public safety education.
