{"title":"自动、鲁棒性和盲视频水印,可抵御摄像头拍摄","authors":"Lina Lin;Deyang Wu;Jiayan Wang;Yanli Chen;Xinpeng Zhang;Hanzhou Wu","doi":"10.1109/TCSVT.2024.3448502","DOIUrl":null,"url":null,"abstract":"As a secondary generation method, video recording will cause irreversible damage to the watermark within the video, which has always been challenging in video forensics. Although many video watermarking methods are reported in the literature, these methods, however, still cannot well resist camera recording. This has motivated the authors in this paper to introduce a new video watermarking method to resist camera recording. For the proposed method, two watermarks, i.e., copyright watermark and synchronization watermark, are embedded into the well-selected frequency domain coefficients. The synchronization watermark is used to ensure that the copyright watermark can be successfully extracted at the decoder side. To extract the copyright watermark without manual assistance, a neural network based segmentation model is applied to identify the watermarked video-playing region in the camera-recorded video. Meanwhile, automatic perspective correction is performed on the watermarked video-playing region so that the watermark information can be extracted accurately. The experiments show that the watermark data can be embedded into the raw video successfully and extracted from the camera-recorded video accurately by applying the proposed method. And, the proposed method significantly outperforms related works in terms of robustness in different scenarios, which has verified the superiority and applicability of the proposed method.","PeriodicalId":13082,"journal":{"name":"IEEE Transactions on Circuits and Systems for Video Technology","volume":"34 12","pages":"13413-13426"},"PeriodicalIF":8.3000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic, Robust, and Blind Video Watermarking Resisting Camera Recording\",\"authors\":\"Lina Lin;Deyang Wu;Jiayan Wang;Yanli Chen;Xinpeng Zhang;Hanzhou Wu\",\"doi\":\"10.1109/TCSVT.2024.3448502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As a secondary generation method, video recording will cause irreversible damage to the watermark within the video, which has always been challenging in video forensics. Although many video watermarking methods are reported in the literature, these methods, however, still cannot well resist camera recording. This has motivated the authors in this paper to introduce a new video watermarking method to resist camera recording. For the proposed method, two watermarks, i.e., copyright watermark and synchronization watermark, are embedded into the well-selected frequency domain coefficients. The synchronization watermark is used to ensure that the copyright watermark can be successfully extracted at the decoder side. To extract the copyright watermark without manual assistance, a neural network based segmentation model is applied to identify the watermarked video-playing region in the camera-recorded video. Meanwhile, automatic perspective correction is performed on the watermarked video-playing region so that the watermark information can be extracted accurately. The experiments show that the watermark data can be embedded into the raw video successfully and extracted from the camera-recorded video accurately by applying the proposed method. And, the proposed method significantly outperforms related works in terms of robustness in different scenarios, which has verified the superiority and applicability of the proposed method.\",\"PeriodicalId\":13082,\"journal\":{\"name\":\"IEEE Transactions on Circuits and Systems for Video Technology\",\"volume\":\"34 12\",\"pages\":\"13413-13426\"},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Circuits and Systems for Video Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10644044/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Circuits and Systems for Video Technology","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10644044/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Automatic, Robust, and Blind Video Watermarking Resisting Camera Recording
As a secondary generation method, video recording will cause irreversible damage to the watermark within the video, which has always been challenging in video forensics. Although many video watermarking methods are reported in the literature, these methods, however, still cannot well resist camera recording. This has motivated the authors in this paper to introduce a new video watermarking method to resist camera recording. For the proposed method, two watermarks, i.e., copyright watermark and synchronization watermark, are embedded into the well-selected frequency domain coefficients. The synchronization watermark is used to ensure that the copyright watermark can be successfully extracted at the decoder side. To extract the copyright watermark without manual assistance, a neural network based segmentation model is applied to identify the watermarked video-playing region in the camera-recorded video. Meanwhile, automatic perspective correction is performed on the watermarked video-playing region so that the watermark information can be extracted accurately. The experiments show that the watermark data can be embedded into the raw video successfully and extracted from the camera-recorded video accurately by applying the proposed method. And, the proposed method significantly outperforms related works in terms of robustness in different scenarios, which has verified the superiority and applicability of the proposed method.
期刊介绍:
The IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) is dedicated to covering all aspects of video technologies from a circuits and systems perspective. We encourage submissions of general, theoretical, and application-oriented papers related to image and video acquisition, representation, presentation, and display. Additionally, we welcome contributions in areas such as processing, filtering, and transforms; analysis and synthesis; learning and understanding; compression, transmission, communication, and networking; as well as storage, retrieval, indexing, and search. Furthermore, papers focusing on hardware and software design and implementation are highly valued. Join us in advancing the field of video technology through innovative research and insights.