{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T22:08:22Z","timestamp":1785967702156,"version":"3.56.0"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T00:00:00Z","timestamp":1529020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai Innovation Action Plan Project","award":["16511101200"],"award-info":[{"award-number":["16511101200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.<\/jats:p>","DOI":"10.3390\/fi10060054","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T11:21:20Z","timestamp":1529061680000},"page":"54","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":123,"title":["StegNet: Mega Image Steganography Capacity with Deep Convolutional Network"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1411-1188","authenticated-orcid":false,"given":"Pin","family":"Wu","sequence":"first","affiliation":[{"name":"School of Computer Science, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6627-2987","authenticated-orcid":false,"given":"Yang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7243-2783","authenticated-orcid":false,"given":"Xiaoqiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Shanghai University, Shanghai 200444, China"},{"name":"Shanghai Institute for Advanced Communication & Data Science, Shanghai University, Shanghai 200444, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/LSP.2006.870357","article-title":"Lsb matching revisited","volume":"13","author":"Mielikainen","year":"2006","journal-title":"IEEE Signal Process. 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