Biometric Watermarking Research Articles

Hiding biometric features in audio signals using Gram-Schmidt orthogonalisation

This paper presents a method of imperceptibly inserting a biometric-based digital watermark generated from iris image in an audio signal. The use of biometric features as a watermark is proposed in this paper to address the issue of ownership of digital watermark and digital content. There is a need to design special audio watermarking algorithm which can accommodate biometric-based watermark without disturbing robustness and perceptual transparency as biometric-based watermarks are generally larger in size. The algorithm is designed using Gram-Schmidt orthogonalisation in third level detailed coefficients of multi-resolution decomposition to achieve high payload with good robustness such that watermark is not audible to human auditory system. The embedding capacity of the proposed method is evaluated to be 480 bps and the highest SNR achieved is 41.519 dB. Experimental results validate that the biometric watermark extracted even under different attack situations can be identified uniquely in the iris database.

Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems

Digital image watermarking is a major domain for hiding the biometric information, in which the watermark data are made to be concealed inside a host image imposing imperceptible change in the picture. Due to the advance in digital image watermarking, the majority of research aims to make a reliable improvement in robustness to prevent the attack. The reversible invisible watermarking scheme is used for fingerprint and iris multimodal biometric system. A novel approach is used for fusing different biometric modalities. Individual unique modalities of fingerprint and iris biometric are extracted and fused using different fusion techniques. The performance of different fusion techniques is evaluated and the Discrete Wavelet Transform fusion method is identified as the best. Then the best fused biometric template is watermarked into a cover image. The various watermarking techniques such as the Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD) and Bacterial Foraging Optimization Algorithm (BFOA) are implemented to the fused biometric feature image. Performance of watermarking systems is compared using different metrics. It is found that the watermarked images are found robust over different attacks and they are able to reverse the biometric template for Bacterial Foraging Optimization Algorithm (BFOA) watermarking technique.

English

Now days due to advancement of technology & high speed internet, the access and transfer of digital media such as image, text, audio and video becomes easy for everybody. Because of the increased use of internet, the need of security and authenticity has become essential. To secure the multimedia data, digital watermarking techniques have become key tool to tackle the challenges faced by the rapid distribution of digital content over the internet. Digital watermarking is a branch of information hiding which is used to hide proprietary information in digital media like photographs, digital music, digital video etc. In this paper we have developed image watermarking model based on discrete wavelet transform. In this technique, we embed and extract biometric watermark using the alpha blending technique in low frequency band. This paper illustrates a general architecture and modified algorithm for DWT based digital watermarking. To demonstrate working of the aforementioned architecture and algorithm, an experimental system is also developed with different value of alpha at 1,2 and 3 level of DWT. This paper also describes output of the experimental system with their comparison.

Biometric watermarks based on face recognition methods for authentication of digital images

AbstractBecause making digital images secure runs into the substantial challenge of owner authentication, many security schemes based on cryptography, steganography and watermarking technology include biometric recognition methods. To follow on these studies, this paper describes a combination of facial images with watermarking technology to automatically authenticate digital images owners/users. In the proposed methodology, biometric face recognition methods such as principal component analysis and eigenfeature regularization and extraction produce vectorial representations of facial images. These vectors are used as copyright watermarks, in a few common watermarking schemes, and are tested for identification purposes after they are extracted. Initially, watermarking algorithms are studied with some arbitrary cover image, and also the most robust algorithm is tested for different cover images of particular subjects. The strength of this paper is finding relationships between the original and extracted biometric data using neural networks instead of the most common, simple measures such as correlation coefficients or distance metrics. The NN subject identification is performed directly, as there is no need to reconstruct facial images after the watermarks are extracted, compute templates for particular subjects, or seek a suitable distance metric. What is more, the presented study includes a performance comparison of two machine learning methods, frequently used for face recognition, and of a few popular watermarking algorithms. Very promising identification results were obtained in many considered experiments, even those involving attacks on watermarked images. Copyright © 2014 John Wiley & Sons, Ltd.

PSO Watermarking Model for Multimodal Biometric System

Multimodal Biometric Watermarking System using multiple sources of information for establishing the individuality has been widely recognized, computational models for multimodal biometrics recognition have only recently got attention. In this paper multimodal biometric images such as fingerprint, palmprint, and iris are extracted individually and are fused together using Average, Minimum and Maximum fusion mechanism. The fused template is then watermarked using the PSO watermarking system. The biometric features used here are fingerprint, iris and palmprint. The image quality is measured by using various metrics such as Peak Signal Noise ratio(PSNR), Normalized Absolute Error(NAE) and Normalized Cross Correlation(NCC). CASIA database is chosen for the biometric images. All the images are 8 bit gray-level JPEG image with the resolution of 320*280.

Dual watermarking technique with multiple biometric watermarks

In order to improve the robustness of the watermarking algorithm, a dual watermarking method is proposed to prove copyright ownership. Visible watermarking is important for protecting online resources from unauthorized reproduction. However robust, visible watermarks are vulnerable to illegal removal and other common signal processing and geometric attacks. Multiple invisible watermarks can enhance the protection of the visibly watermarked image. When the ownership of tampered image is in question, the invisible watermark can be extracted to provide appropriate ownership information. We have proposed dual watermarking scheme with multiple biometric watermarks in which it embeds speech and face biometric traits of owner invisibly and lastly offline signature is overlaid translucently on image. Before embedding, speech is compressed using Linear Predictive Coding (LPC) and Gabor face is created from face biometric trait. All three watermarks Gabor face, LPC coefficients and offline signature are the biometric characteristics of the owner and hence they are highly related with copyright holder. The proposed scheme is robust enough, Gabor face and LPC coefficients can be extracted from the signature marked image or even from the tampered image from which signature is removed illegally or legally. As multiple watermarks are embedded at least one watermark survives under different attacks. It can find application for joint ownership or to address single ownership multiple times.

Novel Iris Biometric Watermarking Based on Singular Value Decomposition and Discrete Cosine Transform

A novel iris biometric watermarking scheme is proposed focusing on iris recognition instead of the traditional watermark for increasing the security of the digital products. The preprocess of iris image is to be done firstly, which generates the iris biometric template from person's eye images. And then the templates are to be on discrete cosine transform; the value of the discrete cosine is encoded to BCH error control coding. The host image is divided into four areas equally correspondingly. The BCH codes are embedded in the singular values of each host image's coefficients which are obtained through discrete cosine transform (DCT). Numerical results reveal that proposed method can extract the watermark effectively and illustrate its security and robustness.

A blind watermarking algorithm for fingerprint images based on contourlet transform

We present a novel blind biometric watermarking algorithm based on contourlet transform in this paper. The host image is decomposed into a series of multiscale, local and directional subimages using contourlet transform. A high frequency directional sub-band which has the highest energy is used for watermarking. At the same time, the original binary watermark is scrambled by two-dimensional Arnold transform, and then embedded into the selected directional sub-band. The signs of the contourlet coefficients are modified depending on the watermark bit. The watermark retrieving algorithm is a blind detection process, and it does not need the original image. Iris code is taken as the watermark and finger print image is taken as the host image. The simulation results show that the proposed watermarking algorithm gives good perceptual transparency and is able to resist additive channel noise attacks, cropping, histogram equalisation, gamma correction, etc.

Augmenting security of biometric with watermarking

This paper presents a new blind biometric watermarking scheme based on discrete wavelet transform for biometric template protection. We are using fingerprint feature and iris feature as multiple watermarks instead of randomly generated Gaussian noise type watermark. Watermark embedding locations are selected by measuring power level of each coefficient which gives sensibility of human eye to local image perturbations. Though biometric features are embedded sequentially, care is taken that most significant features are embedded to those locations whose power level is maximum which provides more robustness. We embed watermark into mid-frequency sub-band by modifying the amplitude of selected coefficient based on comparison between original and estimated values. Estimated value of coefficient is calculated from average sub-band of the same resolution level of the cover image. A reliable watermark extraction scheme is developed for the extraction of watermark from distorted image. Experimental evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks. We show that the proposed scheme also gives adjustability to the user for selecting P percentage of retained wavelet coefficients.

Biometric template protection with DCT-based watermarking

In this paper we have addressed a solution of two big issues in design of multimodal system: template protection and fusion strategy. A robust biometric watermarking algorithm is proposed for biometric template protection. The fingerprint feature vector and iris features are used as watermark. Proposed DCT-based watermarking technique embeds watermark in low-frequency AC coefficients of selected 8 $$\times $$ × 8 DCT smoother blocks. Blocks are classified based on human visual system. The robustness of the proposed algorithm is compared with the few state-of-art literature when watermarked image is subjected to possible channel attacks. Decision level fusion strategy is used to improve the overall performance of multimodal system. That is achieved by conditionally limiting the threshold of the fingerprint system to a maximum value, obtained by projecting 50 % of the cross over error rate on to the FRR curve of the iris system.

AUTHENTICATION OF DIGITAL DOCUMENTS USING SECRET KEY BIOMETRIC WATERMARKING

Digital documents play a major role in modern era. They are easy to generate, modify and manage. The easy modifiable property of digital document makes it more vulnerable to forgery. It can be easily tampered or forged. So the challenge is to produce digital documents that are highly resistant to forgery and reliably confirms the real owner of the document. This can be resolved by biometric watermarking which make a direct relation between the document and its owner. A new biometric watermarking technique with secret key is proposed to digitize the authoritative documents issued by government / other organizations as a part of UID / Aadhar card project of India using biometric watermarking. Biometric code is generated from the biometric data collected from the owner of the document. The biometric code is watermarked in the document with a secret key to generate a biometric watermarked document that authenticates the real owner. Dewatermarking the document with the same key yields the biometric code that can be used for authentication of the document. If the document is tampered in any way it will be indicated in the extracted watermark. Experimental results show that 100% accuracy is obtained in authenticating the genuine documents.

INFORMATION SECURITY BY BIOMETRIC WATERKING

In the recent digital world information security, person identification and authentication, and network security are the prime implications and off course requirements. To fulfill the above requirement watermarking is used widely with biometric information i.e. face image, iris, fingerprint, signature, hand geometry, voice and key stroke pattern. We propose a method for securing and protecting the biometric information of any person with the help of watermarking. The security can also be improved with the help of secrete key. Now a day’s multimodal biometric watermarking is the most advanced technology for information security where more than one biometric information’s are combined for enhancing security and privacy of information and system. In this paper we used face image as the biometric information to be secured, iris image with specific size with key is used for security improvement.

A robust biometric dual watermarking technique with hand vein patterns for digital images

Intellectual property protection of digital images and multimedia is a prickly and largely unsolved problem. A highly robust biometric, dual watermarking technique using hand vein feature is proposed in this paper for securing digital images. In visible watermarking, the hand vein feature of an individual is embedded to the host image using Discrete Cosine Transform (DCT) in spatial domain. The invisible watermark is then EX-ORed with the bit planes of visible watermarked image. The hand vein biometric characteristic has been used to measure the identity of the recipient. This dual watermarking technique establishes owner's right to the image.

Offline Handwritten Signature based Blind Biometric Watermarking and Authetication Technique using Biorthogonal Wavelet Transform

A method for establishing the identity of an individual is essential in all transactions whether they are commercial or personal. The ability to establish identity with certainty can prevent fraud or forgery. In the midst of an electronic revolution, this remains a major concern in ecommerce, telecommunications, healthcare, and security. In this paper, we present a novel method for biometric image watermarking using the biorthogonal wavelet transform and authentication of the recovered signature from the image data. In proposed approach the offline signature, which is a biometric characteristics of owner is embedded in second level detailed coefficients of discrete wavelet transform of cover image. The novelty of the proposed scheme is that, it also goes a step further wherein it extracts the features of recovered signatures and does the template matching with features of signature data base.

Biometric watermarking via wavelet approach, reverse engineering in info-security and dematerialization of public administration

This research wants to present a highly-performing system to establish the intellectual property of a file by using advanced Information Fusion (IF) techniques. IF is different depending on the fact that it can be a textual file, an image or multimedial; indeed, the further step consists in creating blue biometric codes with different watermarking mechanisms based on wavelet and wavelet packet respectively. Thanks to reverse engineering algorithms which this work have generated, such a system seems particularly appropriate to contexts like Public Administration or production companies which present complex industrial/documental processes where it is relevant to trace the access to information, and the sequence of accesses made by operators/users. Hence it seems also clear that such a system can be useful for all that concerns files/contents distributed online for what regards both the intellectual property and the access/modification/use of information.

Feature based RDWT watermarking for multimodal biometric system

This paper presents a 3-level RDWT biometric watermarking algorithm to embed the voice biometric MFC coefficients in a color face image of the same individual for increased robustness, security and accuracy. Phase congruency model is used to compute the embedding locations which preserves the facial features from being watermarked and ensures that the face recognition accuracy is not compromised. The proposed watermarking algorithm uses adaptive user-specific watermarking parameters for improved performance. Using face, voice and multimodal recognition algorithms, and statistical evaluation, we show that the proposed RDWT watermarking algorithm is robust to different frequency and geometric attacks, and provides the multimodal biometric verification accuracy of 94%.

Enhancing security of fingerprints through contextual biometric watermarking

This paper presents a novel digital watermarking technique using face and demographic text data as multiple watermarks for verifying the chain of custody and protecting the integrity of a fingerprint image. The watermarks are embedded in selected texture regions of a fingerprint image using discrete wavelet transform. Experimental results show that modifications in these locations are visually imperceptible and maintain the minutiae details. The integrity of the fingerprint image is verified through the high matching scores obtained from an automatic fingerprint identification system. There is also a high degree of visual correlation between the embedded images, and the extracted images from the watermarked fingerprint. The degree of similarity is computed using pixel-based metrics and human visual system metrics. The results also show that the proposed watermarked fingerprint and the extracted images are resilient to common attacks such as compression, filtering, and noise.

Robust biometric image watermarking for fingerprint and face template protection

This paper presents a combined DWT and LSB based biometric watermarking algorithm that securely embeds a face template in a fingerprint image. The proposed algorithm is robust to geometric and frequency attacks and protects the integrity of both the face template and the fingerprint image. Experimental results performed on a database of 750 face and 750 fingerprint images show that the algorithm has the advantages of both the existing DWT and LSB based algorithms. A multimodal biometric algorithm is used as a metric to evaluate the combined performance of both face and fingerprint recognition.

Improving biometric recognition accuracy and robustness using DWT and SVM watermarking

This paper presents a novel biometric watermarking algorithm for improving the recognition accuracy and protecting the face and fingerprint images from tampering. Multi-resolution Discrete Wavelet Transform is used for embedding the face image in a fingerprint image. An intelligent learning algorithm based on υ-Support Vector Machine (SVM) is introduced to enhance the quality of the extracted face image. The performance of the watermarking algorithm is experimentally validated using existing fingerprint and face recognition algorithms. The results show that the extracted fingerprint and face images are of high quality. The use of SVM enhances the performance of face recognition by at least 10% even when the watermarked image is subjected to certain geometric and frequency attacks such as scaling, cropping, compression and filtering.