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Watermarking is a process in which both physical and digital media are marked using watermarks in order to protect ownership of the watermarked media. Digital water- marking is a technique where a watermark gets embedded into the carrier signal while preserving the quality of the original media. Embedding can happen in various domains and could be both hidden and plain, but the quality and the information carried by the signal should not deteriorate. This paper deals with hiding watermarks into speech audio signals using deep neural networks. We present an encoder-decoder architecture that achieved PSNR value greater than 57dB, which we used as a preservation measure of the original signal and message transmission accuracy of almost 100%. Audio data, used in this paper, consists of speeches from the Parliament of Montenegro.
Date : 2021-05-05 Size : 1.01mb User : bamzi334

Watermarking is an operation of embedding infor- mation into an image in a way that allows to identify ownership of the image despite applying some distortions on it. In this paper, we present a novel end-to-end solution for embedding and recovering the watermark in the digital image using convolutional neural networks. We propose a spreading method of the message over the spatial domain of the image, hence reducing the local bits per pixel capacity and significantly increasing robustness. To obtain the model we use adversarial training, apply noiser layers between the encoder and the decoder, and implement a precise JPEG approximation. Moreover, we broaden the spectrum of typically considered attacks on the watermark and we achieve high overall robustness, most notably against JPEG compression, Gaussian blur, subsampling or resizing. We show that an appli- cation of some attacks could increase robustness against other non-seen during training distortions across one group of attacks — a proper grouping of the attacks according to their scope allows to achieve high general robustness
Date : 2021-05-05 Size : 756.6kb User : bamzi334
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