Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV...Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.展开更多
The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images.In this study,the Face Swapping Attention Network(FSA-Net)is proposed to generate photoreal-istic face...The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images.In this study,the Face Swapping Attention Network(FSA-Net)is proposed to generate photoreal-istic face swapping.The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint(cheek,mouth,eye,nose,etc.),which causes artifacts and makes the generated face silhouette non-realistic.To address this problem,a novel reinforced multi-aware attention module,referred to as RMAA,is proposed for handling facial fusion and expression occlusion flaws.The framework includes two stages.In the first stage,a novel attribute encoder is proposed to extract multiple levels of target face attributes and integrate identities and attributes when synthesizing swapped faces.In the second stage,a novel Stochastic Error Refinement(SRE)module is designed to solve the problem of facial occlusion,which is used to repair occlusion regions in a semi-supervised way without any post-processing.The proposed method is then compared with the current state-of-the-art methods.The obtained results demonstrate the qualitative and quantitative outperformance of the proposed method.More details are provided at the footnote link and at https://sites.google.com/view/fsa-net-official.展开更多
基金supported in part by National Natural Science Foundation of China(62271096,U20A20157)Natural Science Foundation of Chongqing,China(cstc2020jcyj-zdxmX0024,CSTB2022NSCQMSX0600)+5 种基金University Innovation Research Group of Chongqing(CXQT20017)Program for Innovation Team Building at Institutions of Higher Education in Chongqing(CXTDX201601020)Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000626)Youth Innovation Group Support Program of ICE Discipline of CQUPT(SCIE-QN-2022-04)the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202000626Chongqing Municipal Technology Innovation and Application Development Special Key Project(cstc2020jscx-dxwtBX0053)。
文摘Cyber-Physical Networks(CPN)are comprehensive systems that integrate information and physical domains,and are widely used in various fields such as online social networking,smart grids,and the Internet of Vehicles(IoV).With the increasing popularity of digital photography and Internet technology,more and more users are sharing images on CPN.However,many images are shared without any privacy processing,exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI)algorithms.Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy.To address this issue,we propose a social relationship-driven privacy customization protection model for publishers and co-photographers.We construct a heterogeneous social information network centered on social relationships,introduce a user intimacy evaluation method with time decay,and evaluate privacy levels considering user interest similarity.To protect user privacy while maintaining image appreciation,we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN)to swap faces that need to be protected.Our proposed method minimizes the loss of image utility while satisfying privacy requirements,as shown by extensive theoretical and simulation analyses.
基金supported by the National Natural Science Foundation of China(No.61772179)the Hunan Provincial Natural Science Foundation of China(No.2020JJ4152,No.2022JJ50016)+2 种基金the science and technology innovation Program of Hunan Province(No.2016TP1020)the Scientific Research Fund of Hunan Provincial Education Department(No.21B0649)the Double First-Class University Project of Hunan Province(Xiangjiaotong[2018]469).
文摘The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images.In this study,the Face Swapping Attention Network(FSA-Net)is proposed to generate photoreal-istic face swapping.The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint(cheek,mouth,eye,nose,etc.),which causes artifacts and makes the generated face silhouette non-realistic.To address this problem,a novel reinforced multi-aware attention module,referred to as RMAA,is proposed for handling facial fusion and expression occlusion flaws.The framework includes two stages.In the first stage,a novel attribute encoder is proposed to extract multiple levels of target face attributes and integrate identities and attributes when synthesizing swapped faces.In the second stage,a novel Stochastic Error Refinement(SRE)module is designed to solve the problem of facial occlusion,which is used to repair occlusion regions in a semi-supervised way without any post-processing.The proposed method is then compared with the current state-of-the-art methods.The obtained results demonstrate the qualitative and quantitative outperformance of the proposed method.More details are provided at the footnote link and at https://sites.google.com/view/fsa-net-official.
