Description: 本文设计了一种简单的实用数字水印系统。详细论述了二值数字水印的嵌入与提取算法以及具体实现，并对其抗攻击能力作了一定程度的分析。最后利用MATLAB工具实现了一个简单的基于空域变换与BP神经网络的实用数字水印系统。-In this paper, design a simple and practical digital watermarking system. Discussed in detail binary digital watermark embedding and extraction algorithms as well as the concrete realization, and its anti-attack capability of the analysis to a certain extent. Finally the use of MATLAB tools for the realization of a simple space-based transform and BP neural networks of practical digital watermarking system. Platform: |
Size: 1164288 |
Author:亓先军 |
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Description: 神经网络,数字识别,水印程序,matlab
里面有关于matlab数字识别的多份代码及资料，
-Neural network, digital identification, watermark program, matlab inside matlab figures on the number of identification code and information Platform: |
Size: 7710720 |
Author:刘永 |
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Description: 基于神经网络的数字水印提取,数字图像水印嵌入与提取，自己辛辛苦苦编的。- Study on Digital Image Watermarking Algorithms based on
Artificial Neural Networks
Platform: |
Size: 13312 |
Author:赵小志 |
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Description: 实现基于BP神经网络的数字水印添加和提取的实验。实验中采用了BP神经网络对原始图像进行处理添加水印。-Add the BP neural network-based digital watermarking and extraction experiments. BP neural network add a watermark to the original image processing is used in the experiment. Platform: |
Size: 1024 |
Author:David |
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Description: 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. Platform: |
Size: 1057925 |
Author:bamzi334 |
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Description: 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 Platform: |
Size: 774757 |
Author:bamzi334 |
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Description: Similar to other digital assets, deep neural network
(DNN) models could suffer from piracy threat initiated by insider
and/or outsider adversaries due to their inherent commercial
value. DNN watermarking is a promising technique to mitigate
this threat to intellectual property. This work focuses on black-
box DNN watermarking, with which an owner can only verify
his ownership by issuing special trigger queries to a remote
suspicious model. However, informed attackers, who are aware
of the watermark and somehow obtain the triggers, could
forge fake triggers to claim their ownerships since the poor
robustness of triggers and the lack of correlation between the
model and the owner identity. This consideration calls for new
watermarking methods that can achieve better trade-off for
addressing the discrepancy. In this paper, we exploit frequency
domain image watermarking to generate triggers and build our
DNN watermarking algorithm accordingly. Since watermarking
in the frequency domain is high concealment and robust to
signal processing operation, the proposed algorithm is superior to
existing schemes in resisting fraudulent claim attack. Besides, ex-
tensive experimental results on 3 datasets and 8 neural networks
demonstrate that the proposed DNN watermarking algorithm
achieves similar performance on functionality metrics and better
performance on security metrics when compared with existing
algorithms. Platform: |
Size: 374457 |
Author:bamzi334 |
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Search - neural watermark