Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark
Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perfo...
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doaj-46344a86d34e49c780deeca78abe8f3a2020-11-25T03:28:26ZengMDPI AGApplied Sciences2076-34172020-09-01106854685410.3390/app10196854Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and WatermarkJae-Eun Lee0Young-Ho Seo1Dong-Wook Kim2Department of Electronic Materials Engeering, Kwangwoon University, Seoul 01897, KoreaDepartment of Electronic Materials Engeering, Kwangwoon University, Seoul 01897, KoreaDepartment of Electronic Materials Engeering, Kwangwoon University, Seoul 01897, KoreaDigital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution’s adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host’s resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network’s performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods.https://www.mdpi.com/2076-3417/10/19/6854digital watermarkneural networksinvisibilityrobustnessdigital images |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jae-Eun Lee Young-Ho Seo Dong-Wook Kim |
spellingShingle |
Jae-Eun Lee Young-Ho Seo Dong-Wook Kim Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark Applied Sciences digital watermark neural networks invisibility robustness digital images |
author_facet |
Jae-Eun Lee Young-Ho Seo Dong-Wook Kim |
author_sort |
Jae-Eun Lee |
title |
Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark |
title_short |
Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark |
title_full |
Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark |
title_fullStr |
Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark |
title_full_unstemmed |
Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark |
title_sort |
convolutional neural network-based digital image watermarking adaptive to the resolution of image and watermark |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-09-01 |
description |
Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution’s adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host’s resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network’s performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods. |
topic |
digital watermark neural networks invisibility robustness digital images |
url |
https://www.mdpi.com/2076-3417/10/19/6854 |
work_keys_str_mv |
AT jaeeunlee convolutionalneuralnetworkbaseddigitalimagewatermarkingadaptivetotheresolutionofimageandwatermark AT younghoseo convolutionalneuralnetworkbaseddigitalimagewatermarkingadaptivetotheresolutionofimageandwatermark AT dongwookkim convolutionalneuralnetworkbaseddigitalimagewatermarkingadaptivetotheresolutionofimageandwatermark |
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