Book ChapterDOI
A Deep Neural Network Approach for Classification of Watermarked and Non-watermarked Images
Sreenivas Sremath Tirumala,Noreen Jamil,M. G. Abbas Malik +2 more
- pp 779-784
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TLDR
Deep Autoencoders, a form of deep neural networks for classification and identification of watermarked and non-watermarked images is proposed and the experiment results show that,Deep neural networks performed better that traditional feed forward neural networks.Abstract:
Digital watermarking is the process of embedding an unique mark into digital data to prevent counterfeit. With the exponential increase in the data, the process of segregating a watermarked and non-watermarked images is very time consuming. It is necessary to automate the process of differentiating a watermarked and a non-watermarked images as well as identifying whether the given image is watermarked or not for identifying the authenticity. In this paper, we propose to use Deep Autoencoders, a form of deep neural networks for classification and identification of watermarked and non-watermarked images. The experiments are carried out using NWND dataset originally with 444 images. These images are watermarked using image, shape and text watermarking techniques to make the entire dataset to 1776 images. The experiment results show that, deep neural networks performed better that traditional feed forward neural networks. The classification accuracies with Original - IW for DAEN and ANN are 77.9% and 25.9 % respectively. Whereas for Original - SW and Original - TW, it is 82.1% and 32.7%, 64.2% and 20.06% respectively. The DAEN was able to identify 86 images correctly out of 100 images supplied which is 86% of accuracy with an average training rmse of 0.06423 and testing rmse of 0.0784.read more
Citations
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An Investigation on Ability of Pre-trained Convolutional Neural Networks Trained on ImageNet to Classify Melanoma Images Without Re-training
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References
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Journal ArticleDOI
Multimedia watermarking techniques
Frank Hartung,Martin Kutter +1 more
TL;DR: The basic concepts of watermarking systems are outlined and illustrated with proposed water marking methods for images, video, audio, text documents, and other media.
Proceedings Article
Image Denoising and Inpainting with Deep Neural Networks
Junyuan Xie,Linli Xu,Enhong Chen +2 more
TL;DR: A novel approach to low-level vision problems that combines sparse coding and deep networks pre-trained with denoising auto-encoder (DA) is presented and can automatically remove complex patterns like superimposed text from an image, rather than simple patterns like pixels missing at random.
Proceedings ArticleDOI
Embedding Watermarks into Deep Neural Networks
TL;DR: This work proposes to use digital watermarking technology to protect intellectual property and detect intellectual property infringement in the use of trained models, and proposes a general framework for embedding a watermark in model parameters, using a parameter regularizer.
BookDOI
Multimedia Security Handbook
Borko Furht,Darko Kirovski +1 more
TL;DR: This work discusses protection of Multimedia Content in Distribution Networks, Digital Watermarking Framework: Applications, Parameters and Requirements, and Lossless Data Hiding: Fundamentals, Algorithms and Applications.
Journal ArticleDOI
Digital watermarking for deep neural networks
TL;DR: A digital watermarking technology for ownership authorization of deep neural networks, which can embed a watermark during the training of a deep neural network from scratch, and during fine-tuning and distilling, without impairing its performance.