scholarly journals Robust and Blind Multiple Image Watermarking Using CNN and DWT in Video

2020 ◽  
Vol 6 (3) ◽  
pp. 8-13
Author(s):  
Farha Khan ◽  
M. Sarwar Raeen
Keyword(s):  
Neural Network ◽  
Wavelet Coefficient ◽  
Image Watermarking ◽  
High Capacity ◽  
Multimedia Systems ◽  
Convolution Neural Network ◽  
Discrete Wavelet ◽  
Host Image ◽  
Different Types ◽  
Result Analysis

Digital watermarking was introduced as a result of rapid advancement of networked multimedia systems. It had been developed to enforce copyright technologies for cover of copyright possession. Due to increase in growth of internet users of networks are increasing rapidly. It has been concluded that to minimize distortions and to increase capacity, techniques in frequency domain must be combined with another technique which has high capacity and strong robustness against different types of attacks. In this paper, a robust multiple watermarking which combine Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT)and Convolution Neural Network techniques on selected middle band of the video frames is used. This methodology is considered to be robust blind watermarking because it successfully fulfills the requirement of imperceptibility and provides high robustness against a number of image-processing attacks such as Mean filtering, Median filtering, Gaussian noise, salt and pepper noise, poison noise and rotation attack. The proposed method embeds watermark by decomposing the host image. Convolution neural network calculates the weight factor for each wavelet coefficient. The watermark bits are added to the selected coefficients without any perceptual degradation for host image. The simulation is performed on MATLAB platform. The result analysis is evaluated on PSNR and MSE which is used to define robustness of the watermark that means that the watermark will not be destroyed after intentional or involuntary attacks and can still be used for certification. The analysis of the results was made with different types of attacks concluded that the proposed technique is approximately 14% efficient as compared to existing work.

scholarly journals Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark

2020 ◽  
Vol 10 (19) ◽  
pp. 6854 ◽  
Author(s):  
Jae-Eun Lee ◽  
Young-Ho Seo ◽  
Dong-Wook Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Data ◽  
Digital Images ◽  
Image Watermarking ◽  
Value Added ◽  
Absolute Error ◽  
Geometric Attacks ◽  
Host Image ◽  
Processing Network

Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution’s adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host’s resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network’s performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods.

scholarly journals Various Image Processing Attacks for Image Watermarking in the Wavelet Domain Using Singular Value Decomposition and Discrete Cosine Transform

2021 ◽  
Vol 8 (2) ◽  
pp. 51-59
Author(s):  
Riyajuddin ◽  
Arikera Padmanabha Reddy
Keyword(s):  
Singular Value Decomposition ◽  
Digital Media ◽  
Discrete Cosine Transform ◽  
Image Watermarking ◽  
Speckle Noise ◽  
Singular Value ◽  
Multimedia Systems ◽  
Discrete Wavelet ◽  
Cosine Transform ◽  
Value Decomposition

The dispersal of digital media due to the fast evolution of networked multimedia systems has created an essential need for copyright prompting technologies that can protect multimedia objects such as text, images, audio and videos from copyright ownership. This paper proposes digital image watermarking algorithm for copyright protection based on discrete wavelet transform, discrete cosine transform and singular value decomposition. In this method a watermark is embedded into the low frequency sub-band of a host image, after subjecting the watermarked image to various attacks like Gaussian noise, rotation sharpening, noise and pepper salt and speckle noise etc., we extract the originally inserted watermark images from LL sub-band by Truncated singular value decomposition and compare them on the basis of their mean square error, peak signal to noise ratio and normalized correlation values. Experimental results are provided to illustrate that the proposed scheme is the robustness of the technique on wide set of attacks.

scholarly journals A Robust and Oblivious Watermarking Method Using Maximum Wavelet Coefficient Modulation and Genetic Algorithm

2020 ◽  
Author(s):  
Surya Prasada Rao Borra ◽  
Kongara Ramanjaneyulu ◽  
K. Raja Rajeswari
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Coefficient ◽  
Image Watermarking ◽  
Substantial Improvement ◽  
Performance Comparison ◽  
Cover Image ◽  
Discrete Wavelet ◽  
Content Authentication ◽  
Binary Images ◽  
Watermark Embedding

An image watermarking method using Discrete Wavelet Transform (DWT) and Genetic Algorithm (GA) is presented for applications like content authentication and copyright protection. This method is robust to various image attacks. For watermark detection/extraction, the cover image is not essential. Gray scale images of size 512 × 512 as cover image and binary images of size 64 × 64 as watermark are used in the simulation of the proposed method. Watermark embedding is done in the DWT domain. 3rd and 2nd level detail sub-band coefficients are selected for further processing. Selected coefficients are arranged in different blocks. The size of the block and the number blocks depends on the size of the watermark. One watermark bit is embedded in each block. Then, inverse DWT operation is performed to get the required watermarked image. This watermarked image is used for transmission and distribution purposes. In case of any dispute over the ownership, the hidden watermark is decoded to solve the problem. Threshold-based method is used for watermark extraction. Control parameters are identified and optimized based on GA for targeted performance in terms of PSNR and NCC. Performance comparison is done with the existing works and substantial improvement is witnessed.

scholarly journals Artificial Intelligence – State of Art Convolution Neural Network Architectures in a Nutshell

2019 ◽  
Vol 8 (11S) ◽  
pp. 1278-1280
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolution Neural Network ◽  
Network Architectures ◽  
Deep Architecture ◽  
Different Types ◽  
Neuro Imaging ◽  
Brain Data ◽  
Configuration Changes ◽  
The Brain

It is a well-known fact that all the Artificial Intelligence (AI)researches happening across multiple verticals such as Neuro Imaging, Computer Vision, Deep learning etc point to one master goal of modelling the human brain function by understanding how each part of the brain works. The Convolution neural network (CNN) is one of best deep architecture suitable to handle variety of inputs. In this paper we explore the different types of input data the CNN deep architecture can process and some of the CNN configuration changes that has proved good Accuracy. We have highlighted those specialized CNN architectures along with different types of data inputs they handle including the Functional Magnetic Resonance (fMRI) Neuro Image brain data input.

scholarly journals High-capacity and Robust Image Watermarking Algorithm

2021 ◽  
Author(s):  
Zheng Liu ◽  
Xi-Yan Li ◽  
Prof. Qing-Lei ◽  
Hanqing Sun ◽  
Weimin Lian ◽  
...  
Keyword(s):  
Digital Media ◽  
Wavelet Transforms ◽  
Image Watermarking ◽  
High Capacity ◽  
Discrete Wavelet ◽  
Security Issue ◽  
Discrete Cosine Transforms ◽  
Position Information ◽  
Quad Tree ◽  
Robust Image

Abstract With the development of internet, digital media can be manipulated, reproduced, and distributed conveniently over networks. However, illegal copy, transmission and distribution of digital media become an important security issue. In this paper, we propose a high-capacity and robustness method based on discrete wavelet transforms(DWT) and optimal discrete cosine transforms(DCT). We present two approaches, they are: DWT-ODCT(high-capacity and robust image watermarking algorithm based on DWT and optimal DCT), and(P-DWT-ODCT) high-capacity and robust image watermarking algorithm based on DWT and optimal DCT by watermark preprocessing. The watermark image is preprocessed by halftone and quad tree techniques, and the position information about the content is extracted as the actual embedded value. The cover image is transformed by DWT and optimal DCT, which provides high imperceptibility and the least image distortion. Watermark bits are not directly inserted into the frequency coefficient, but embedded by modifying the coefficient according to some rules. With this method, regardless of the approach used, our study are higher capacity and robustness than the existing schemes. The watermark extraction produces high image quality after a variety of attacks.

scholarly journals Deep Learning Based Indian Currency Detection for Visually Challenged using VGG16

2020 ◽  
Vol 9 (2) ◽  
pp. 769-972
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Visually Impaired ◽  
Convolution Neural Network ◽  
Visually Impaired People ◽  
Impaired People ◽  
Learning Technique ◽  
Different Types ◽  
Visually Challenged ◽  
Banknote Recognition

Banknote recognition is a major problem faced by visually Challenged people. So we propose a system to help the visually Challenged people to identify the different types of Indian currencies through deep learning technique. In our proposed project, bank notes with different positions are directly fed into VGG 16, a pretrained model of convolution neural network which extracts deep features. From our work the visually impaired people will be able to recognize different types if Indian Currencies.

scholarly journals Automatic Diseases Detection and Classification in Maize Crop using Convolution Neural Network

2021 ◽  
Vol 10 (2) ◽  
pp. 675-679
Keyword(s):  
Neural Network ◽  
Plant Diseases ◽  
Convolution Neural Network ◽  
Matlab Simulation ◽  
Cercospora Leaf Spot ◽  
Maize Crop ◽  
Maize Production ◽  
Different Types ◽  
The Common

Maize is a major crop in Pakistan and it plays an important role in the economy of the country. However different types of plant diseases could affect both the quality and quantity of maize production. To cope with such issues, the majority of the farmers still depend on traditional methods, which are expensive, time-consuming, laborious, and not very effective. To address the issues, we proposed a Convolution Neural Network (CNN) based solution for the detection and classification of different types of maize diseases. We used a publicly available free dataset of 4000 images. The images were classified into four categories. The first three categories represent the Common rust, Cercospora leaf spot grey, and Northern leaf blightand diseases, while the last category represents normal leaves. To test, implement, and evaluate the performance of our proposed method, we used a MATLAB simulation environment. We also compared our results with two other solutions, available in the literature. Our solution achieved 96.53 % accuracy. From the results, we concluded that the proposed method could be used for the automatic detection and classification of different types of maize diseases.

scholarly journals A Novel Dual Image Watermarking Technique Using Homomorphic Transform and DWT

2020 ◽  
Vol 30 (1) ◽  
pp. 297-311
Author(s):  
Priyank Khare ◽  
Vinay Kumar Srivastava
Keyword(s):  
Image Watermarking ◽  
Singular Values ◽  
Discrete Wavelet ◽  
Image Simulation ◽  
Arnold Transform ◽  
Host Image ◽  
Dual Image ◽  
A New Technique ◽  
Value Decomposition ◽  
Ownership Rights

Abstract In this paper a new technique of dual image watermarking is proposed for protection of ownership rights which utilizes salient properties of homomorphic transform (HT), discrete wavelet transform (DWT), singular value decomposition (SVD) and Arnold transform (AT). In embedding algorithm host image is splitted into reflectance and illumination components using HT, DWT is further applied to the reflectance component resulting in frequency subbands (HL and LH) which are transformed by SVD. Two image watermarks are selected for embedding process whereas security of proposed algorithm is strengthen by performing scrambling of second watermark through AT. Both watermarks are transformed with DWT and SVD. Singular values (SVs) of both transformed watermark are embedded into SVs of host image. Simulation results clearly signifies for high robustness and imperceptibility of proposed algorithm as it is examined under various attacks. Superiority of proposed technique is illustrated by comparing it with other reported methods.

scholarly journals Multiple Watermarking for Images using Back-Propagation Neural Network and DWT

2019 ◽  
Vol 9 (1) ◽  
pp. 4088-4093
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Image Watermarking ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Discrete Wavelet ◽  
Multiple Watermarking ◽  
Segmented Image ◽  
Multiple Watermarks

An effective multiple watermarking technique supported on neural network into the wavelet transform can be proposed. The wavelet coefficients has been preferred by Human Visual System. In the proposed work focus on Discrete Wavelet Transform based segmented image watermarking techniques using Back-Propagation neural networks. Using improved BPNN, the multiple watermarks are embedded into the original image, which can advance the pace of the learn, reduce the error and the qualified neural networks are extricate multiple watermarks as of the embedded images. The planned strategy achieves a excellent visual effect scheduled the watermarked images as well as high robustness on extracted multiple watermarks.

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