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: 不用到matlab工具箱，进行神经网络控制器设计，采用BP方法-Do not have to matlab toolbox, the neural network controller design, using BP method Platform: |
Size: 1024 |
Author:海朝 |
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Description: 文章展示了一种新的方法用于功率系统中短期负载预测。提出的方案使用混沌时间序列分析基于确定性混沌去捕捉复杂的负载行为特征。确定性的混沌允许我们重构一个时间序列并决定输入的变量个数。这篇文章描述了混沌时间序列对日间功率系统峰值的分析。确定性混沌的非线性图形通过多层感知器的神经网络得到。提出的方案在一个例子中具体阐述。-This paper presents a new approach to short-term load forecasting in power systems. The proposed method makes use of chaos time series analysis that is based on deterministic chaos to capture characteristics of complicated load behavior. Deterministic chaos allows us to reconstruct a time series and determine the number of input variables. This paper describes chaos time series analysis of daily power system peak loads. The nonlinear
mapping of deterministic chaos is identified by the multilayer perceptron of an artificial neural network. The proposed approach is demonstrated in an example. Platform: |
Size: 382976 |
Author:will |
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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: 工神经网络（Artificial Neural Network）又称连接机模型，是在现代神经学、生物学、心理学等学科研究的基础上产生的，它反映了生物神经系统处理外界事物的基本过程，是在模拟人脑神经组织的基础上发展起来的计算系统，是由大量处理单元通过广泛互联而构成的网络体系，它具有生物神经系统的基本特征，在一定程度上反映了人脑功能的若干反映，是对生物系统的某种模拟，具有大规模并行、分布式处理、自组织、自学习等优点，被广泛应用于语音分析、图像识别、数字水印、计算机视觉等很多领域，取得了许多突出的成果。最近由于人工神经网络的快速发展，它已经成为模式识别的强有力的工具。神经网络的运用展开了新的领域，解决其它模式识别不能解决的问题，其分类功能特别适合于模式识别与分类的应用。多层前向BP网络是目前应用最多的一种神经网络形式-Artificial neural network (Artificial Neural Network) connection, also known as machine model, is based on interdisciplinary research in modern neurology, biology, psychology, etc. produced on, it reflects the fundamental processes of biological neural processing of external things, is in the simulation developed on the basis of human brain tissue computing system is constituted by a large number of processing units interconnected through an extensive network system, it has the basic characteristics of biological neural systems, to a certain extent reflects the number of reflecting the human brain function is simulation of certain biological systems, with massively parallel, distributed processing, self-organizing, self-learning, etc., are widely used in many areas of speech analysis, image recognition, digital watermarking, computer vision, and achieved many outstanding achievements . Recently due to the rapid development of artificial neural networks, it has become a powerful tool fo Platform: |
Size: 100352 |
Author:沈阳阳 |
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Description: BP神经网络用于函数拟合与模式识别，非常适合计算机视觉方面的研究使用,实现了图像的加水印，去噪，加噪声等功能。- BP neural network function fitting and pattern recognition, Very suitable for the study using computer vision, Realize image watermarking, de-noising, plus noise and other functions. Platform: |
Size: 6144 |
Author:鲍启祥 |
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Description: 实现了图像的加水印，去噪，加噪声等功能，用MATLAB实现动态聚类或迭代自组织数据分析，BP神经网络用于函数拟合与模式识别。- Realize image watermarking, de-noising, plus noise and other functions, Using MATLAB dynamic clustering or iterative self-organizing data analysis, BP neural network function fitting and pattern recognition. Platform: |
Size: 10240 |
Author:yang |
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Description: 基于人工神经网络的常用数字信号调制，本程序的性能已经达到较高水平，基于小波变换的数字水印算法matlab代码。- The commonly used digital signal modulation based on artificial neural network, The performance of the program has reached a high level, Based on wavelet transform digital watermarking algorithm matlab code. Platform: |
Size: 7168 |
Author:张庭兵 |
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Description: 仿真效率很高的，实现了图像的加水印，去噪，加噪声等功能，是一种双隐层反向传播神经网络。- High simulation efficiency, Realize image watermarking, de-noising, plus noise and other functions, Is a two hidden layer back propagation neural network. Platform: |
Size: 5120 |
Author:gingsun |
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Description: 实现了图像的加水印，去噪，加噪声等功能，是一种双隐层反向传播神经网络，是信号处理的基础。- Realize image watermarking, de-noising, plus noise and other functions, Is a two hidden layer back propagation neural network, Is the basis of the signal processing. Platform: |
Size: 7168 |
Author:bui |
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Description: A Robust Blind Audio Watermarking Scheme Based on Singular Value Decomposition and Neural Networks Platform: |
Size: 238592 |
Author:Rajan007 |
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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 - watermarking neural