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摘要:
Deep neural network has proven to be very effective in computer vision fields.Deep convolutional network can learn the most suitable features of certain images without specific measure functions and outperform lots of traditional image processing methods.Generative adversarial network(GAN)is becoming one of the highlights among these deep neural networks.GAN is capable of generating realistic images which are imperceptible to the human vision system so that the generated images can be directly used as intermediate medium for many tasks.One promising application of using GAN generated images would be image concealing which requires the embedded image looks like not being tampered to human vision system and also undetectable to most analyzers.Texture synthesizing has drawn lots of attention in computer vision field and is used for image concealing in steganography and watermark.The traditional methods which use synthesized textures for information hiding mainly select features and mathematic functions by human metrics and usually have a low embedding rate.This paper takes advantage of the generative network and proposes an approach for synthesizing complex texture-like image of arbitrary size using a modified deep convolutional generative adversarial network(DCGAN),and then demonstrates the feasibility of embedding another image inside the generated texture while the difference between the two images is nearly invisible to the human eyes.
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篇名 Embedding Image Through Generated Intermediate Medium Using Deep Convolutional Generative Adversarial Network
来源期刊 计算机、材料和连续体(英文) 学科 工学
关键词 GAN CNN texture synthesis STEGANOGRAPHY WATERMARK IMAGE concealing information hiding
年,卷(期) 2018,(8) 所属期刊栏目
研究方向 页码范围 313-324
页数 12页 分类号 TP3
字数 语种
DOI
五维指标
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研究主题发展历程
节点文献
GAN
CNN
texture
synthesis
STEGANOGRAPHY
WATERMARK
IMAGE
concealing
information
hiding
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
计算机、材料和连续体(英文)
月刊
1546-2218
江苏省南京市浦口区东大路2号东大科技园A
出版文献量(篇)
346
总下载数(次)
4
总被引数(次)
0
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