基于深度神经网络的图像水印数据自恢复分析

S. Bhalerao, I. Ansari, Adarsh Kumar
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摘要

世界正在迅速走向数字化,数据安全是人们最关心的问题。数字图像构成了这些数字数据的很大一部分。因此,数字图像水印等技术被用于保证图像的安全性。本文研究了基于深度神经网络的压缩算法在彩色图像篡改检测与自恢复系统中的应用。采用基于深度神经网络的图像压缩技术对主机图像进行压缩,并在主机图像中嵌入多个压缩后的图像副本,实现自恢复。由于基于DNN的压缩算法提供了更小的尺寸和更好的质量,因此水印图像具有较高的PSNR(峰值信噪比)。同样,恢复图像的质量也很高。针对不同的图像和篡改条件,分析了基于深度神经网络的算法。在最好的情况下,该算法可以从大约88%的篡改中恢复图像。
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Analysis of DNN based image watermarking data generation for self-recovery
The world is rapidly going digital and data security is the foremost concern. Digital images form a large chunk of this digital data. Therefore, techniques like digital image watermarking are used for assuring image security. In this paper application of DNN based compression algorithm is examined for implementation of color image tampering detection and self-recovery system. The DNN based image compression is used to compress the host images and multiple copies of compressed images are embedded in the host image for self-recovery. Due to the smaller size and better quality provided by DNN based compression algorithm, the watermarked images have high PSNR (peak signal to noise ratio). Similarly, the quality of recovered images is also high. The DNN based algorithm is analyzed for different images and tampering conditions. In the best-case scenario, the algorithm can recover the image from about 88% tampering.
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