{"title":"An image embedding in image by a complexity based region segmentation method","authors":"M. Niimi, H. Noda, E. Kawaguchi","doi":"10.1109/ICIP.1997.631986","DOIUrl":null,"url":null,"abstract":"This paper describes a new technique to embed secret data into a dummy image by using image segmentation based on a local complexity measure. The key idea to this approach is that a binary image can be categorized as \"informative\" and \"noise-like\" regions which are segmented by a \"complexity measure\". If the embedding data is noise-like, we can hide it in the noise-like region of the dummy image. If a part of embedding data is simple, then we apply \"image conjugate\" operation to it. This operation transform a simple pattern into a complex pattern. In our experiment, we could embed two color images in a 512/spl times/512 (8 bits/pixel) size gray image (which was dummy) without losing any information. The total amount of the two embedded images was 115 KB, which was about 45% of the dummy image.","PeriodicalId":92344,"journal":{"name":"Computer analysis of images and patterns : proceedings of the ... International Conference on Automatic Image Processing. International Conference on Automatic Image Processing","volume":"16 1","pages":"74-77 vol.3"},"PeriodicalIF":0.0000,"publicationDate":"1997-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"48","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer analysis of images and patterns : proceedings of the ... International Conference on Automatic Image Processing. International Conference on Automatic Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.1997.631986","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 48
Abstract
This paper describes a new technique to embed secret data into a dummy image by using image segmentation based on a local complexity measure. The key idea to this approach is that a binary image can be categorized as "informative" and "noise-like" regions which are segmented by a "complexity measure". If the embedding data is noise-like, we can hide it in the noise-like region of the dummy image. If a part of embedding data is simple, then we apply "image conjugate" operation to it. This operation transform a simple pattern into a complex pattern. In our experiment, we could embed two color images in a 512/spl times/512 (8 bits/pixel) size gray image (which was dummy) without losing any information. The total amount of the two embedded images was 115 KB, which was about 45% of the dummy image.
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