2017
DOI: 10.1016/j.cose.2016.11.016
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Exploring the learning capabilities of convolutional neural networks for robust image watermarking

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Cited by 158 publications
(102 citation statements)
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“…The experimental results show the effectiveness of the proposed watermarking method. Future work will focus on investigating novel image watermarking algorithms, such as deep learning [31][32], compressive sensing, and game theory.…”
Section: Resultsmentioning
confidence: 99%
“…The experimental results show the effectiveness of the proposed watermarking method. Future work will focus on investigating novel image watermarking algorithms, such as deep learning [31][32], compressive sensing, and game theory.…”
Section: Resultsmentioning
confidence: 99%
“…However, their assistive application for the watermarking process is more recent. For example, Kandi et al [25] applied two auto-encoder CNN structures for feature extraction to be separately used for positive and negative embedding. The same auto-encoder networks are used at the receiver side to obtain the feature maps and extract the watermark data.…”
Section: Related Workmentioning
confidence: 99%
“…Among all the machine learning tools, deep networks and Convolutional Neural Nets (CNN), have gained the most widespread attention in a large variety of computer vision applications such as pattern recognition [22], image classification [23] and object detection [24]. Very recently a few works have emerged about the application of deep networks in watermarking [25,26,27]. Kandi et al[25] proposed CNN based auto-encoder structures to hide watermark data in their feature maps.…”
mentioning
confidence: 99%
“…With increasing demands for copyright protection, digital watermarking techniques have been developed rapidly in recent years [4][5][6][7]. Efforts have been made to design various watermark embedding algorithms for digital images to improve the robustness and imperceptibility of the watermark [8][9][10][11][12]. The abundant studies on image watermarking provide valuable information and references for other digital products as well [13][14][15][16], and thus enhance the development of watermarking for vector maps.…”
Section: Introductionmentioning
confidence: 99%