StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

Pin Wu, Yang Yang and Xiaoqiang Li
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Pin Wu: School of Computer Science, Shanghai University, Shanghai 200444, China
Yang Yang: School of Computer Science, Shanghai University, Shanghai 200444, China
Xiaoqiang Li: School of Computer Science, Shanghai University, Shanghai 200444, China

Future Internet, 2018, vol. 10, issue 6, 1-15

Abstract: 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.

Keywords: convolutional neural network; image steganography; steganography capacity (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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