scholarly journals A Novel Blind Digital Watermarking Based on SVD and Extreme Learning Machine

2017 ◽  
Vol 10 (1) ◽  
pp. 160-167
Author(s):  
Neelam Dabas ◽  
Rampal Singh ◽  
Vikash Chaudhary
Keyword(s):  
Digital Media ◽  
Extreme Learning Machine ◽  
Signal To Noise Ratio ◽  
Extraction Process ◽  
Digital Data ◽  
Learning Machine ◽  
Value Decomposition ◽  
Cropping Rotation ◽  
Integer Wavelet ◽  
Watermarking Scheme

Modification of media and illegal production is a big problem now a days because of free availability of digital media. Protection and securing the digital data is a challenge. An Integer Wavelet Transformation (IWT) domain based robust watermarking scheme with Singular Value Decomposition (SVD) and Extreme Learning Machine (ELM) have been proposed and tested on different images. In this proposed scheme, a watermark or logo is embedded in the IWT domain as ownership information with SVD and ELM is trained to learn the relationship between the original coefficient and the watermarked one. This trained ELM is used in the extraction process to extract the embedded logo from the image. Experimental results show that the proposed watermarking scheme is robust against various image attacks like Blurring, Noise, Cropping, Rotation, Sharpening etc. Performance analysis of proposed watermarking scheme is measured with Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER)

scholarly journals Semi-Fragile Watermarking with Tamper Localization and Recovery using AMBTC Method

2020 ◽  
Vol 9 (5) ◽  
pp. 54-59
Keyword(s):  
Digital Media ◽  
Extraction Process ◽  
Fragile Watermarking ◽  
Discrete Wavelet ◽  
Tamper Localization ◽  
Absolute Moment ◽  
Media Communication ◽  
Value Decomposition ◽  
Watermarking Scheme

The watermarking scheme in digital media communication has become an essential tool in helping content creators prove ownership if any dispute arises in copyright infringement. In this paper, Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) is performed in the watermarking process which improves the authentication of the image and the detection of the tampered region. The semi-fragile watermarking scheme provides robustness to the watermark during extraction process even after the unintentional compression attacks like JPEG compression. The watermark payload is significantly lowered in order to increase the quality of the image. The recovery is done using the absolute moment block truncation coding (AMBTC) of the image in the untampered region. Thus, both the mean and first absolute moment are used in recovering the tampered regions in the watermarked image.

Joint Encryption and Compression-Based Watermarking Technique for Security of Digital Documents

2021 ◽  
Vol 21 (1) ◽  
pp. 1-20
Author(s):  
A. K. Singh ◽  
S. Thakur ◽  
Alireza Jolfaei ◽  
Gautam Srivastava ◽  
MD. Elhoseny ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Digital Data ◽  
The Internet ◽  
Discrete Wavelet ◽  
Compression Performance ◽  
Strong Compression ◽  
Multiple Watermarks ◽  
Value Decomposition

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through non-sub-sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them.

A sequential feature extraction method based on discrete wavelet transform, phase space reconstruction, and singular value decomposition and an improved extreme learning machine for rolling bearing fault diagnosis

2017 ◽  
Vol 232 (6) ◽  
pp. 635-649 ◽  
Author(s):  
DZ Li ◽  
X Zheng ◽  
QW Xie ◽  
QB Jin
Keyword(s):  
Fault Diagnosis ◽  
Phase Space ◽  
Extreme Learning Machine ◽  
Rolling Bearing ◽  
Singular Value ◽  
Discrete Wavelet ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Learning Machine ◽  
Value Decomposition

A novel fault diagnosis approach based on a combination of discrete wavelet transform, phase space reconstruction, singular value decomposition, and improved extreme learning machine is presented in rolling bearing fault identification and classification. The proposed method provides proper solutions for improving the accuracy of faults classification. To achieve this goal, initial signals are divided into sub-band wavelet coefficients using discrete wavelet transform. Then, each of sub-band is mapped into three-dimensional space using the phase space reconstruction method to completely describe characteristics in the high dimension. Thereafter, singular values are calculated by singular value decomposition method, which demonstrate crucial variances in original vibration signal. Lastly, an improved extreme learning machine is adopted as a classifier for fault classification. The proposed method is applied to the rolling bearing fault diagnosis with non-linear and non-stationary characteristics. Based on outputs of the improved extreme learning machine, the working condition and fault location could be determined accurately and quickly. Achieved results, compared with other schemes, show that the proposed scheme in this article can be regarded as an effective and reliable method for rolling bearing fault diagnosis.

scholarly journals A Robust Watermarking Scheme for Online Multimedia Copyright Protection Using New Chaotic Map

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Amir Anees ◽  
Iqtadar Hussain ◽  
Abdulmohsen Algarni ◽  
Muhammad Aslam
Keyword(s):  
Digital Media ◽  
Secure Communication ◽  
Initial Conditions ◽  
Chaotic Map ◽  
Logistic Map ◽  
Digital Data ◽  
Robust Watermarking ◽  
Unified Framework ◽  
Watermarking Scheme ◽  
Randomness Tests

The protection of copyrights of digital media uploaded to the Internet is a growing problem. In this paper, first, we present a unified framework for embedding and detecting watermark in digital data. Second, a new robust watermarking scheme is proposed considering this concern. The proposed work incorporates three chaotic maps which specify the location for embedding the watermark. Third, a new chaotic map, the Extended Logistic map, is proposed in this work. The proposed map has a bigger range than logistic and cubic maps. It has shown good results in a bifurcation, sensitivity to initial conditions, and randomness tests. Furthermore, with the detailed analysis of initial parameters, it is justified that Extended Logistic map can be used in secure communication, particularly watermarking. Fourth, to check the robustness of proposed watermarking scheme, we have done a series of analyses and standard attacks. The results confirm that the proposed watermarking scheme is robust against visual and statistical analysis and can resist the standard attacks.

scholarly journals Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

2020 ◽  
Vol 12 (22) ◽  
pp. 3762
Author(s):  
Ping Zhou ◽  
Gang Chen ◽  
Mingwei Wang ◽  
Jifa Chen ◽  
Yizhe Li
Keyword(s):  
Extreme Learning Machine ◽  
Extraction Process ◽  
Acoustic Backscatter ◽  
Sediment Classification ◽  
Sediment Types ◽  
Learning Machine ◽  
Reliability And Robustness ◽  
Seabed Sediments ◽  
Residual Errors

Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or auto encoder. In addition, the performance of the existing classifiers is limited by such incorrect information, meaning it is difficult to achieve fine classification in survey areas. Therefore, we propose a sediment classification method based on the acoustic backscatter image by combining a stacked denoising auto encoder (SDAE) and a modified extreme learning machine (MELM). The SDAE is used to extract the deep-seated sediment features, so that the training network can automatically learn to remove the residual errors from the original image. The MELM model, which integrates weighted estimation, a Parzen window and particle swarm optimization, is applied to weaken the interference of mislabeled samples on the training network and to optimize the random expression of input layer parameters. The experimental results show that the SDAE-MELM method greatly reduces mutual interference between sediment types, while the sediment boundaries are clear and continuous. The reliability and robustness of the proposed method are better than with other approaches, as assessed by the overall classification effect and comprehensive indexes.

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