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一种抗屏摄攻击的DCT域深度水印方法

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数字水印技术在多媒体保护方面发挥着巨大的作用,实际应用需求的变更推动了数字水印技术的发展.目前,基于深度学习的水印技术在鲁棒性上有了较大的提升,但水印的嵌入基本在空域进行,载体图像的失真仍然比较明显.此外,现有方法在面对摄屏攻击时效果不佳.为解决上述问题,提出了一种抗屏摄攻击的DCT域深度水印方法.该模型由DCT层、编码器、解码器和屏摄模拟层组成.DCT层将图像的Y分量转换为DCT域,然后编码器通过端到端训练修改DCT系数,将秘密消息嵌入到图像中.这种频域嵌入方法使得水印信息能够分布到图像的整个空间,从而减少了失真效应.此外,还提出了 一个噪声层,用于模拟屏摄过程中特殊的摩尔纹和反光效果.训练过程分为两个阶段:在第一阶段,编码器和解码器进行端到端的训练;而在第二阶段,屏摄模拟层和传统的失真攻击被用来增强水印图像,然后使用失真水印图像来进一步优化解码器.大量的实验结果表明,该模型具有较高的透明度和鲁棒性,并且在屏摄鲁棒性方面优于其他方法.
Screen-shooting Resilient DCT Domain Watermarking Method Based on Deep Learning
Digital watermarking technology plays an important role in multimedia protection,and the various demands for practi-cal applications promotes the development of digital watermarking technology.Recently,the robustness of the deep learning-based watermarking model has been greatly improved,but the embedding process is mostly carried out in the spatial domain,and this causes obvious distortions to original images.In addition,existing methods do not work well under the screen-shooting attack.To solve the above problems,this paper proposes a deep learning-based DCT domain watermarking method which is robust to the screen-shooting attack.This model consists of a DCT layer,an encoder,a decoder,and a screen shoot simulation layer.The DCT layer converts the Y component of images into the DCT domain,then the encoder embeds secret messages into the image by mo-difying the DCT coefficients through end-to-end training.This embedding method in the frequency domain makes the watermark information to be distributed to the whole space of images so that the distortion effect is reduced.Furthermore,we propose a noise layer to simulate moiré and light reflection effects,which are common distortions in the screen-shooting attack.The training process is splitted into two stages.In the first stage,the encoder and decoder are trained end-to-end.While in the second stage,the screen-shooting simulation layer and traditional distortion attacks are used to augment the watermarked image,then we use the distorted watermarked image to furtheroptimize the decoder.Extensive experimental results show that the proposed model has high transparency and robustness,and is superior to other methods in screen robustness.

Digital watermarkDeep learningDCT TransformImperceptibilityScreen-shooting robustness

黄昌喜、赵成鑫、姜骁腾、凌贺飞、刘辉

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华中科技大学计算机科学与技术学院 武汉 430074

数字水印 深度学习 DCT变换 不可感知性 屏摄鲁棒性

国家自然科学基金国家重点研发计划国家重点研发计划湖北省重点研发计划湖北省重点研发计划武汉基础研究知识创新项目中国博士后科学基金

619721692019QYY02022022YFB26018022022BAA0462022BAA04220200106010121822022M711251

2024

10.11896/jsjkx.221200121

计算机科学
重庆西南信息有限公司（原科技部西南信息中心）

计算机科学

CSTPCD北大核心
影响因子：0.944
ISSN：1002-137X
年,卷(期)：2024.51(2)
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