A Comprehensive Review on Digital Image Watermarking

The advent of the Internet led to the easy availability of digital data like images, audio, and video. Easy access to multimedia gives rise to the issues such as content authentication, security, copyright protection, and ownership identification. Here, we discuss the concept of digital image watermarking with a focus on the technique used in image watermark embedding and extraction of the watermark. The detailed classification along with the basic characteristics, namely visual imperceptibility, robustness, capacity, security of digital watermarking is also presented in this work. Further, we have also discussed the recent application areas of digital watermarking such as healthcare, remote education, electronic voting systems, and the military. The robustness is evaluated by examining the effect of image processing attacks on the signed content and the watermark recoverability. The authors believe that the comprehensive survey presented in this paper will help the new researchers to gather knowledge in this domain. Further, the comparative analysis can enkindle ideas to improve upon the already mentioned techniques

BAT Algorithm-Based Multi-Class Crop Leaf Disease Prediction Bootstrap Model

In the task of identification of infected agriculture plants, the leaf-based disease identification technique is especially effective in better understand crop disease among various techniques to detect infection. Recognition of an infected leaf image from healthy images gets encumbered when the model is required to detect the type of leaf disease. This paper presents a BAT-based crop disease prediction bootstrap model (BCDPBM) that identifies the health of the leaf and performs disease prediction. The BAT algorithm in the proposed model increases the capability of the Gaussian mixture model for foreground region detection. Furthermore, in the work, the co-occurrence matrix feature and histogram feature are extracted for the training of the bootstrap model. Hence, leaf foreground detection by the BAT algorithm with the Gaussian mixture improves the feature extraction quality for bootstrap learning. The proposed model utilizes a dataset of real leaf images for conducting experiments. The results of the model are compared with different existing models across various parameters. The results show the prediction accuracy enhancement of multiclass leaf disease using the BCDPBM model

A Study of Data Security on E-Governance using Steganographic Optimization Algorithms

Steganography has been used massively in numerous fields to maintain the privacy and integrity of messages transferred via the internet. The need to secure the information has augmented with the increase in e-governance usage. The wide adoption of e-governance services also opens the doors to cybercriminals for fraudulent activities in cyberspace. To deal with these cybercrimes we need optimized and advanced steganographic techniques. Various advanced optimization techniques can be applied to steganography to obtain better results for the security of information. Various optimization techniques like particle swarm optimization and genetic algorithms with cryptography can be used to protect information for e-governance services. In this study, a comprehensive review of steganographic algorithms using optimization techniques is presented. A new perspective on using this technique to protect the information for e-governance is also presented. Deep Learning might be the area that can be used to automate the steganography process in combination with other method

Tamper detection of qur'anic text watermarking scheme based on vowel letters with Kashida using exclusive-or and queueing technique

The most sensitive Arabic text available online is the digital Holy Qur’an. This sacred Islamic religious book is recited by all Muslims worldwide including the non-Arabs as part of their worship needs. It should be protected from any kind of tampering to keep its invaluable meaning intact. Different characteristics of the Arabic letters like the vowels ( أ . و . ي ), Kashida (extended letters), and other symbols in the Holy Qur’an must be secured from alterations. The cover text of the al-Qur’an and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio (PSNR), Embedding Ratio (ER), and Normalized Cross-Correlation (NCC), thus the location for tamper detection gets low accuracy. Watermarking technique with enhanced attributes must therefore be designed for the Qur’an text using Arabic vowel letters with Kashida. Most of the existing detection methods that tried to achieve accurate results related to the tampered Qur’an text often show various limitations like diacritics, alif mad surah, double space, separate shapes of Arabic letters, and Kashida. The gap addressed by this research is to improve the security of Arabic text in the Holy Qur’an by using vowel letters with Kashida. The purpose of this research is to enhance Quran text watermarking scheme based on exclusive-or and reversing with queueing techniques. The methodology consists of four phases. The first phase is pre-processing followed by the embedding process phase to hide the data after the vowel letters wherein if the secret bit is ‘1’, insert the Kashida but do not insert it if the bit is ‘0’. The third phase is extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR (for the imperceptibility), ER (for the capacity), and NCC (for the security of the watermarking). The experimental results revealed the improvement of the NCC by 1.77 %, PSNR by 9.6 %, and ER by 8.6 % compared to available current schemes. Hence, it can be concluded that the proposed scheme has the ability to detect the location of tampering accurately for attacks of insertion, deletion, and reordering