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

Human recognition with biometrics is a rapidly emerging area of computer vision. Compared to other well-known biometric features such as the face, fingerprint, iris, and palmprint, the ear has recently received considerable research attention. The ear recognition system accepts 2D or 3D images as input. Since pose, illumination, and scale all affect 2D ear images, it is evident that they all impact recognition performance; therefore, 3D ear images are employed to address these issues. The geometric shapes of 3D ears are utilized as rich features to improve recognition accuracy. We present recent advances in several areas relevant to 3D ear recognition and provide directions for future research. To the best of our knowledge, no comprehensive review has been conducted on using 3D ear images in human recognition. This review focuses on three primary categories of 3D ear recognition techniques: (1) registration-based recognition, (2) local and global feature-based recognition, and (3) a combination of (1) and (2). Based on the above categorization and publicly available 3D ear datasets, this article reviews existing 3D ear recognition techniques.

A Survey of 3D Ear Recognition Techniques

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

Behind the prevalence of multimedia technology, digital copyright disputes are becoming increasingly serious. The digital watermarking prevention technique against the copyright infringement needs to be improved urgently. Among the proposed technologies, zero-watermarking has been favored recently. In order to improve the robustness of the zero-watermarking, a novel robust audio zerowatermarking method based on sparse representation is proposed. The proposed scheme is mainly based on the K-singular value decomposition (K-SVD) algorithm to construct an optimal over complete dictionary from the background audio signal. After that, the orthogonal matching pursuit (OMP) algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix. Then, the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected, and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking. Experimental results show that the proposed audio zero-watermarking algorithm based on sparse representation performs effectively in resisting various common attacks. Compared with the baseline works, the proposed method has better robustness.

A novel robust zero-watermarking algorithm for audio based on sparse representation

This paper presents a novel contact-less palmprint biometric hardware security approach for securing DSP based coprocessors. The proposed approach is capable of generating secret biometric palmprint constraints that are embedded in DSP designs used in consumer electronics systems to detect counterfeited versions. Any DSP based intellectual property (IP) core can be embedded with proposed palmprint signature to distinguish between authentic and its fake versions. In the proposed approach, the authentic biometric palmprint is first converted into its equivalent palmprint signature representing digital template based on palmprint feature set, followed by embedding palmprint signature’s digital template into the design in the form of secret biometric constraints. The biometric palmprint constraints generated through the proposed approach is non-replicable and non-vulnerable compared to other signature based scheme such as hardware steganography and hardware watermarking. The proposed approach exploits several security features such as: palmprint nodal points, palmprint features, palmprint feature ordering, concatenation mechanism of features etc to generate a robust palmprint hardware security constraint. The results of the proposed approach indicated stronger probability of co-incidence and stronger tamper tolerance ability than recent related works.

Contact-Less Palmprint Biometric for Securing DSP Coprocessors Used in CE Systems

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 analysis of kin relations from visual data represents a challenging research problem with important real-world applications. However, research in this area has mostly been limited to the analysis of facial images, despite the potential of other physical (human) characteristics for this task. In this paper, we therefore study the problem of kinship verification from ear images and investigate whether salient appearance characteristics, useful for this task, can be extracted from ear data. To facilitate the study, we introduce a novel dataset, called KinEar, that contains data from 19 families with each family member having from 15 to 31 ear images. Using the KinEar data, we conduct experiments using a Siamese training setup and 5 recent deep learning backbones. The results of our experiments suggests that ear images represent a viable alternative to other modalities for kinship verification, as 4 out of 5 considered models reach a performance of over 60% in terms of the Area Under the Receiver Operating Characteristics (ROC-AUC).

Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models

The Biometric incorporates pictures, for example, iris filters, fingerprints, palm prints which have fine highlights of uniqueness for every person. Since, these pictures are functional in numerous applications, for example, get to control, military, CBI examination, ID, and so forth, its security is a significant concern. A methodology of verifying biometric pictures can be Digital Watermarking. Customary watermarking can mutilate the picture that causes data loss. Indeed, a little contortion can leave biometric of an individual unrecognizable. So here is an urgent requirement for a watermarking plan that doesn't misshape the original picture by any means. This paper presents a watermarking of zero bit strategy in which one of a kind distinguishing proof codes are produced from biometric pictures by applying Discrete Wavelet Transform (DWT) trailed by Singular Value Decomposition (SVD). Exploratory outcomes delineate that exceptional highlights separated are profoundly steady even by various picture preparing attacks and henceforth declare its value for classified biometric pictures.

Wavelet-SVD based Zero-Bit Watermarking for Securing Biometric Images

The security of a system through biometric features is one of the acceptable trends to which every researcher recommends. Such biometric features contain features of iris, fingerprint, face, etc. Today, these security traits are widely exploited by an imposter to trace unlicensed access to the system. So it demands to secure these features since foremost alarming challenges are raises when these trustworthy features are compromised. This paper endorses the design of zero-bit watermarking in which a user’s unique id is integrated with his facial features in the multi-transform domain. The user’s unique ID is used to establish true authentication of the host facial features and also to recognize the face without any segmentation techniques. To make this effort tangible, Singular values are calculated of some particular frequency coefficients of the host image that are generated using FFT transformation in which a number of appropriate regions of the host image are selected based on their frequency of information. These Singular values of host facial image are calculated using SVD in which watermark (image) user’s unique ID is integrated correctly while maintaining the equilibrium among imperceptibility, robustness, and payload. The resultant watermarked image is tested against various image processing attacks and satisfying results are conducted to assure robustness of the model. Thus, the model is indeed satisfying security of the host biometric of the user.

Ankita Dwivedi

Papers

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

Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models

A Zero watermarking Approach for Biometric Image Security

Wavelet-SVD based Zero-Bit Watermarking for Securing Biometric Images

FFT-Based Zero-Bit Watermarking for Facial Recognition and Its Security