With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to mult...With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.展开更多
False monitoring information is a major problem in process production system and several ineffective methods have been proposed to identify false monitoring information in the production system. In this paper, a new m...False monitoring information is a major problem in process production system and several ineffective methods have been proposed to identify false monitoring information in the production system. In this paper, a new method is proposed to identify false monitoring information based on system coupling analysis and collision detection from the perspective of data analysis. Coupling multifractal features are extracted to reflect the changes in coupling relationship by utilizing the multifractal detrended cross-correlation analysis (MF-DXA). Each monitoring variable in process production system has more than one coupled variable, which can be regarded as multi-source. To achieve low redundancy in features and uniform description of coupling relationship, the feature level information fusion is studied based on modified Mahalanobis Taguchi system (MTS). False alarms are identified when the coupling relationships among the coupled monitoring variables collide. Analysis results of coupled R?ssler and Henon datasets indicate the feasibility of this method for selecting the effective coupling feature and uniform description of coupling relationship. The compressor system case of Coal Chemical Ltd. Group is studied and false monitoring information is identified.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
New Rules to Label Al-generated Content to Increase Transparency,A new directive aimed at the AI sector has been released recently to increase transparency and user safety by enforcing mandatory labeling of Al-generat...New Rules to Label Al-generated Content to Increase Transparency,A new directive aimed at the AI sector has been released recently to increase transparency and user safety by enforcing mandatory labeling of Al-generated content.China's National Internet Information Office(NIIO),the Ministry of Industry and Information Technology and public security and broadcast agencies issued the finalized rules on March 14,which will come into force from September 1.The notice,Measures for the Labeling of AI-Generated Content Identification,comes in response to the spread of false information,Al-related fraud and the misuse of technologies which are becoming rife amid the fast development of AI,the NIIO said.展开更多
Demand response transactions between electric consumers,load aggregators,and the distribution network manager based on the"combination of price and incentive"are feasible and efficient.However,the incentive ...Demand response transactions between electric consumers,load aggregators,and the distribution network manager based on the"combination of price and incentive"are feasible and efficient.However,the incentive payment of demand re-sponse is quantified based on private information,which gives the electric consumers and load aggregators the possibility of defrauding illegitimate interests by declaring false information.This paper proposes a method based on Vickrey-Clark-Groves(VCG)theory to prevent electric consumers and load aggregators from taking illegitimate interests through deceptive behaviors in the demand response transactions.Firstly,a demand response transaction framework with the price-and-incentive com-bined mode is established to illustrate the deceptive behaviors in the demand response transactions.Then,the idea for eradi-cating deceptive behaviors based on VCG theory is given,and a detailed VCG-based mathematical model is constructed follow-ing the demand response transaction framework.Further,the proofs of incentive compatibility,individual rationality,cost minimization,and budget balance of the proposed VCG-based method are given.Finally,a modified IEEE 33-node system and a modified IEEE 123-node system are used to illustrate and validate the proposed method.展开更多
文摘With the explosive growth of false information on social media platforms, the automatic detection of multimodalfalse information has received increasing attention. Recent research has significantly contributed to multimodalinformation exchange and fusion, with many methods attempting to integrate unimodal features to generatemultimodal news representations. However, they still need to fully explore the hierarchical and complex semanticcorrelations between different modal contents, severely limiting their performance detecting multimodal falseinformation. This work proposes a two-stage detection framework for multimodal false information detection,called ASMFD, which is based on image aesthetic similarity to segment and explores the consistency andinconsistency features of images and texts. Specifically, we first use the Contrastive Language-Image Pre-training(CLIP) model to learn the relationship between text and images through label awareness and train an imageaesthetic attribute scorer using an aesthetic attribute dataset. Then, we calculate the aesthetic similarity betweenthe image and related images and use this similarity as a threshold to divide the multimodal correlation matrixinto consistency and inconsistencymatrices. Finally, the fusionmodule is designed to identify essential features fordetectingmultimodal false information. In extensive experiments on four datasets, the performance of the ASMFDis superior to state-of-the-art baseline methods.
基金supported by the National Natural Science Foundation of China (Grant No. 51375375)
文摘False monitoring information is a major problem in process production system and several ineffective methods have been proposed to identify false monitoring information in the production system. In this paper, a new method is proposed to identify false monitoring information based on system coupling analysis and collision detection from the perspective of data analysis. Coupling multifractal features are extracted to reflect the changes in coupling relationship by utilizing the multifractal detrended cross-correlation analysis (MF-DXA). Each monitoring variable in process production system has more than one coupled variable, which can be regarded as multi-source. To achieve low redundancy in features and uniform description of coupling relationship, the feature level information fusion is studied based on modified Mahalanobis Taguchi system (MTS). False alarms are identified when the coupling relationships among the coupled monitoring variables collide. Analysis results of coupled R?ssler and Henon datasets indicate the feasibility of this method for selecting the effective coupling feature and uniform description of coupling relationship. The compressor system case of Coal Chemical Ltd. Group is studied and false monitoring information is identified.
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New Rules to Label Al-generated Content to Increase Transparency,A new directive aimed at the AI sector has been released recently to increase transparency and user safety by enforcing mandatory labeling of Al-generated content.China's National Internet Information Office(NIIO),the Ministry of Industry and Information Technology and public security and broadcast agencies issued the finalized rules on March 14,which will come into force from September 1.The notice,Measures for the Labeling of AI-Generated Content Identification,comes in response to the spread of false information,Al-related fraud and the misuse of technologies which are becoming rife amid the fast development of AI,the NIIO said.
基金supported by the Fundamental Research Funds for the Central Universities(No.B230201048).
文摘Demand response transactions between electric consumers,load aggregators,and the distribution network manager based on the"combination of price and incentive"are feasible and efficient.However,the incentive payment of demand re-sponse is quantified based on private information,which gives the electric consumers and load aggregators the possibility of defrauding illegitimate interests by declaring false information.This paper proposes a method based on Vickrey-Clark-Groves(VCG)theory to prevent electric consumers and load aggregators from taking illegitimate interests through deceptive behaviors in the demand response transactions.Firstly,a demand response transaction framework with the price-and-incentive com-bined mode is established to illustrate the deceptive behaviors in the demand response transactions.Then,the idea for eradi-cating deceptive behaviors based on VCG theory is given,and a detailed VCG-based mathematical model is constructed follow-ing the demand response transaction framework.Further,the proofs of incentive compatibility,individual rationality,cost minimization,and budget balance of the proposed VCG-based method are given.Finally,a modified IEEE 33-node system and a modified IEEE 123-node system are used to illustrate and validate the proposed method.
