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Survey on video Security:Examining Threats,Challenges,and Future Trends

Computers, Materials & Continua 2024年第9期80卷 3591-3635页

作者： Ali Asghar Amna Shifa Mamoona Naveed Asghar Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science College of Science and EngineeringUniversity of GalwayGalwayH91 TK33Ireland

videos represent the most prevailing form of digital media for communication,information dissemination,and ***,theirwidespread use has increased the risks of unauthorised access andmanipulation,posing significant *** response,various protection approaches have been developed to secure,authenticate,and ensure the integrity of digital *** study provides a comprehensive survey of the challenges associated with maintaining the confidentiality,integrity,and availability of video content,and examining how it can be *** then investigates current developments in the field of video security by exploring two critical research ***,it examine the techniques used by adversaries to compromise video data and evaluate their *** these attack methodologies is crucial for developing effective defense ***,it explores the various security approaches that can be employed to protect video data,enhancing its transparency,integrity,and *** compares the effectiveness of these approaches across different use cases,including surveillance,video on demand(VoD),and medical videos related to disease ***,it identifies potential research opportunities to enhance video data protection in response to the evolving threat *** this investigation,this study aims to contribute to the ongoing efforts in securing video data,providing insights that are vital for researchers,practitioners,and policymakers dedicated to enhancing the safety and reliability of video content in our digital world.

关键词： Attacks threats security services video manipulation video security

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iHair Recolorer: deep image-to-video hair color transfer

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Science China(Information Sciences) 2021年第11期64卷 52-66页

作者： Keyu WU Lingchen YANG Hongbo FU Youyi ZHENG State Key Laboratory of CAD&CG Zhejiang University School of Creative Media City University of Hong Kong

In this paper, we present i Hair Recolorer, the first deep-learning based approach for examplebased hair color transfer in videos. Given an input video and a reference image, our method automatically transfers the hair color in the reference image to the hair in the video while keeping other hair attributes(e.g.,shape, structure, and illumination) untouched, producing vivid color-transferred dynamic hair in the *** method performs the color transfer purely in the image space, without any form of intermediate 3 D hair reconstruction. The key enabler of our method is a carefully designed conditional generative model that explicitly disentangles various hair attributes into their corresponding sub-spaces, which are implemented as conditional modules integrated into a generator. We introduce a novel spatially and temporally normalized luminance map to represent the structure and illumination of the hair. Such a representation can largely ease the burden of the generator to synthesize temporally coherent vivid dynamic hairs in the video. We further introduce a cycle consistency loss to enforce the faithfulness of the generated results with respect to the reference. We demonstrate our system’s superiority in video hair color transfer by extensive experiments and comparisons to alternative methods.

关键词： hair color transfer video manipulation luminance map cycle consistency Cycle Consistency

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