{"title":"基于感知分析的统计音频水印算法","authors":"Xiaomei Quan, Hongbin Zhang","doi":"10.1145/1102546.1102565","DOIUrl":null,"url":null,"abstract":"In this paper, we describe a novel statistical audio watermarking scheme. Under the control of the masking thresholds, watermark is embedded adaptively and transparently in the perceptual significant portions in wavelet packet domain by a statistical method. Watermark detection can be done without access to the original signal. Experimental results show the proposed scheme can survive common signal manipulations and malicious attacks.","PeriodicalId":124354,"journal":{"name":"ACM Digital Rights Management Workshop","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Statistical audio watermarking algorithm based on perceptual analysis\",\"authors\":\"Xiaomei Quan, Hongbin Zhang\",\"doi\":\"10.1145/1102546.1102565\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we describe a novel statistical audio watermarking scheme. Under the control of the masking thresholds, watermark is embedded adaptively and transparently in the perceptual significant portions in wavelet packet domain by a statistical method. Watermark detection can be done without access to the original signal. Experimental results show the proposed scheme can survive common signal manipulations and malicious attacks.\",\"PeriodicalId\":124354,\"journal\":{\"name\":\"ACM Digital Rights Management Workshop\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Digital Rights Management Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1102546.1102565\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Digital Rights Management Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1102546.1102565","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Statistical audio watermarking algorithm based on perceptual analysis
In this paper, we describe a novel statistical audio watermarking scheme. Under the control of the masking thresholds, watermark is embedded adaptively and transparently in the perceptual significant portions in wavelet packet domain by a statistical method. Watermark detection can be done without access to the original signal. Experimental results show the proposed scheme can survive common signal manipulations and malicious attacks.