Digital copyright protection signifies the technology of rights protection, utilization control, and management of digital substances in the activity of production, propagation, retailing, and consumption. In the era of digital boom in information technology and multimedia, malicious exploitations and piracy have become a global phenomenon; thus, the need for digital content protection is inexorable. Image watermarking has been advocated universally to resolve innumerable dilemmas correlated with issues in the fields of digital rights management and multimedia security. According to anticipated applications, various suitable image watermarking procedures have been designed to safeguard the copyright of digital subjects. In this study, contemporary developments of digital image watermarking techniques are appraised so as to identify state-of-the-art practices and their limitations. This research contribution will be expedient for the academics in instigating efficient watermarking techniques for copyright authentication.

Recent trends in image watermarking techniques for copyright protection: a survey

The issue of ownership of digital watermark is addressed in this paper. An audio watermarking scheme is presented where the biometric pattern of iris is used to generate the watermark that has a stamp of ownership. The watermark is embedded in the high-energy regions selectively in wavelet domain that makes the embedding process robust against signal processing attacks. The watermark extraction process is blind and does not require the original host audio signal. Experimental results of listening evaluation and robustness tests have confirmed that the proposed scheme maintains good perceptual property and offers strong resistance to typical signal processing attacks.

Blind watermarking in audio signals using biometric features in wavelet domain

Deep neural network is a new type of learning algorithm, which has both global and local aspects and performs well in pattern recognition and computational speed. In recent years, deep neural network algorithm has been widely used in scientific research and real life, but its complexity, parallelism and other characteristics lead it to be a very challenging and innovative research area. This study briefly introduces the basic principles and theoretical knowledge of deep neural network algorithms, and mainly discusses their applications and Advancement of feature extraction in the field.

https://doi.org/10.1088/1742-6596/2146/1/012001

A Study on the Application and the Advancement of Deep Neural Network Algorithm

Unauthorized entry in the database is turning very prevalent in digital felony. The obligatory and foremost priority is to shield the data when it is dispersed in a cloud server. The incentive of this work Commences with emphasizing the prevention of outlawed access to characteristics of data practiced by cybercriminals. Favor to adopt biometrics becomes one of the trending subjects when it comes to preventing illegitimate access over digital media because of its unique features. This paper preface the concept of zero-bit watermarking in the digital palm-print image. Many work-related to iris and fingerprint have already done and still alarming concerns are rising. Paramount priority of accepting modern data set confers us to figure out novel and rich features of palm-print which constraints on unlicensed access. The Digital pattern is obtained from unique features of the palm-print image without causing any disfigurement on it. This work utilizes SVD and DWT for the realization of zero watermarking since this method is very substantial contrary to diverse image processing menace. As the attributes of palm-print are unique, so the duplicity of data is eradicated by this proposed method.

A Zero watermarking Approach for Biometric Image Security

At present, the facial expression recognition model in video communication has problems such as weak network generalization ability and complex model structure, which leads to a large amount of computation. Firstly, the Inception architecture is adopted as a design philosophy. The Visual Geometry Group Network (VGGNet) model is improved. Multiscale kernel convolutional layers are constructed to obtain more expressive features. Secondly, the attention mechanism is integrated into a multiscale feature fusion network to form a multiattention mechanism Convolutional Neural Network (CNN) model. Novel spatial and multichannel attention models are designed. The effects of redundant information and noise are reduced. Finally, experiments are carried out on the Fer2013 dataset and the Extended Cohn-Kanade Dataset (CK+) to verify the detection accuracy of the model. The results show that the Delivered Duty Unpaid (DDU) loss can be used for facial expression recognition in complex environments. After the attention module is added, the overall recognition accuracy of the network on Fer2013 and CK+ has been improved to varying degrees. The addition of the channel attention module has a more obvious effect on the recognition accuracy compared with the spatial attention module. The addition of the attention module enables the network to increase the attention to error-prone samples. The improved network model can better extract the key features of facial expressions, enhance the feature discrimination ability, and improve the recognition accuracy of error-prone expressions. The accuracy rate of facial expression recognition with larger movements is over 98%. Facial expressions are an important way of communication between people, and online video has greatly limited this communication method. The proposed CNN model based on multiscale feature fusion will effectively solve these network limitations and have an important and positive impact on future network information exchange.

/pdf/emotion-recognition-of-online-education-learners-by-2lhqs9p1.pdf

Emotion Recognition of Online Education Learners by Convolutional Neural Networks

Protection of data is becoming a crucial task in digital world. This paper proposes a concept for securing biometric data using zero watermarking. Biometric data like a fingerprint, iris, etc. in which unique characters reside, but it may lead to an undesired situation if an attacker tries to copy it from a distributed database. The paper introduces a zero-bit watermarking scheme in iris image. Unlike in conventional watermarking, the watermarked image gets tempered due to distortion of pixels of an image. This paper proposes a robust zero-bit watermarking scheme in which a digital pattern is generated using a unique features from the iris image without tampering the host image. This method uses SVD and DWT for performing zero-bit watermarking and making it stable against several image processing attacks. The features of iris are unique, so the developed algorithm prevents data from duplicity or piracy issues.

An Efficient and Robust Zero-Bit Watermarking Technique for Biometric Image Protection

The objective of this article is to increase the efficiency of face recognition in the aspect of a dynamic frame. Studies were carried out that focus on maintaining the high detection rate as well as increasing the accuracy. The study was conducted by considering the current recording of the dynamic frame of randomly selected people walking on a treadmill. Here, the variety of facial features was studied and extracted using different viewing angles on the subject. The existence of a huge amount of datasets and economical processing power is subject to value enhancement in the presence of a convolutional neural network (CNN) on different object identification and realization criteria. These methodologies with excellent knowledge of deep learning techniques that subjects to enhance the potential of the machine to learn the face values. CNN is capable to identify faces, positive positioning facial benchmarks, estimating postures and understanding faces in interconnected images and dynamic video frames. In this article, we present a novel phase detector technique that is rapid and has the potential of recognizing faces with huge changes. This is performed by a technique of using adopted CNN and recurrent neural network's subpart long‐short term memory technique in a particular way so that the objective of bringing improvement in the reorganization procedure can be fulfilled. And this was completed by getting 97.5%–98.1% of accuracy is sustained in this.

A proficient approach for face detection and recognition using machine learning and high-performance computing

Concealment of biometric features widely attempted in the domain of watermarking of digital data. This draft is the representation of such an example in which fusion of iris (host image) and palm print image (watermark image) has been undergone into a robust and reliable watermarking scheme leaving unobjectionable distortion. These two biometric are logically fused by using the combination DWT (Discrete wavelet transform), DCT (Discrete cosine transform) and PBFO (Parallel bacterial foraging optimization). These 3 techniques are processed for the embedding of palm print image into the iris scan biometrics. The palm-print watermark preserves the ownership of host iris. A combination of both will be used for complete authentication. The quality check of the watermarked image and the watermark (after extraction) has been done by using standard approaches such as PSNR, NCC, BER, and MSE. The results say that the property of the iris image is unchanged after embedding the palm print in it and the image quality of palm print after the extraction process is also contented even after applying attacks. The rate of imperceptibility of host image after embedding is not only qualifying the standards but also conquer among the previous algorithm. The approach of this paper is to realize the concept of dual authentication which enhances the security of user’s media.

Ankit Kumar

Papers

An Efficient and Robust Zero-Bit Watermarking Technique for Biometric Image Protection

A proficient approach for face detection and recognition using machine learning and high-performance computing

A Zero watermarking Approach for Biometric Image Security

DWT, DCT and PBFO based Approach for Biometric Image Security

Biometric Facial Detection and Recognition Based on ILPB and SVM