Biometric Template Spoofing Detection Using Sparse Watermarking Scheme

No Abstrac

Security of Biometric Data Using Compressed Watermarking Technique

This paper has focus on biometric data security over open communication channel of biometric system. Here biometric data is encoded using cs theory and wavelet based embedding technique. The biometric data is convert into encoded sparse measurements which is generating using SVD, random seed and uniform quantization process. Then these encoded sparse measurements are embedding into the host color biometric data using wavelet based watermarking technique. This proposed technique has explored dimension reduction and computational security provided by compressive sensing. This proposed technique has also helps to compressed and to send secret data over noisy communication channel of biometric system against various attacks. The proposed technique provides more security compare to existed technique in literature due to CS theory. The novelty of proposed technique is that, watermark iris image information is compressed and encoded using CS theory and uniform quantization.DOI:http://dx.doi.org/10.11591/ijece.v4i5.664

Dual Learning Model for Multiclass Brain Tumor Classification

A brain tumor occurs in the human body when the brain develops abnormal cells. Tumors are called either benign (noncancerous) or malignant (cancerous). The function of the nervous system is affected by the growth rate and the location of the tumor. The tumor treatment depends on tumor type, size, and location. Artificial intelligence has been widely used to automatically predict various brain tumors using multiple imaging technologies such as magnetic resonance imaging (MRI) and computerized tomography (CT) scan during the last few years. This paper applies a hybrid learning based classifier on an MRI dataset containing benign and malignant images. Moreover, deep learning is also applied to the same dataset. The proposed learning approach’s performance is compared to other existing supervised machine learning approaches. The experimental results show that our proposed approach outperforms the existing approaches available in the literature

Robust and Secure Watermarking Using Sparse Information of Watermark for Biometric Data Protection

Biometric based human authentication system is used for security purpose in many organizations in the present world. This biometric authentication system has several vulnerable points. Two of vulnerable points are protection of biometric templates at system database and protection of biometric templates at communication channel between two modules of biometric authentication systems. In this paper proposed a robust watermarking scheme using the sparse information of watermark biometric to secure vulnerable point like protection of biometric templates at the communication channel of biometric authentication systems. A compressive sensing theory procedure is used for generation of sparse information on watermark biometric data using detail wavelet coefficients. Then sparse information of watermark biometric data is embedded into DCT coefficients of host biometric data. This proposed scheme is robust to common signal processing and geometric attacks like JPEG compression, adding noise, filtering, and cropping, histogram equalization. This proposed scheme has more advantages and high quality measures compared to existing schemes in the literature

A deep neural network and machine learning approach for retinal fundus image classification

Diabetes is a common chronic disease and a major public health problem approaching epidemic proportions globally. People with diabetes are more likely to suffer from glaucoma than people without diabetes. Glaucoma can lead to loss of vision if not diagnosed at an early stage. This study proposes an intelligent computer-aided triage system with a deep neural network and machine learning to develop and analyze color retinal fundus images and classify glaucomatous retinal images. Deep features of retinal images from the fundus retinal image are extracted using a deep neural network, and the classification of features is performed and analyzed using different machine learning classifiers. Experimental results show that the combination of deep neural network and logistic regression-based classifier outperforms all existing glaucomatous triage systems, improving classification accuracy, sensitivity, and specificity

Robust watermarking technique using different wavelet decomposition levels for signature image protection

This paper proposed a non-blind watermarking technique based on different wavelet decomposition levels for biometric image protection.In this technique, a biometric image is used as a watermark instead of a standard image, logo or random noise pattern type watermark.For watermark embedding, the original host image and the watermark biometric image are transformed into various levels of wavelet coefficients.The watermark biometric image is embedded into the host image by modifying the values of the wavelet coefficients of the host image using the values of wavelet coefficients of the watermark biometric image.Experimental results demonstrated that the proposed technique was able to withstand various watermarking attacks. The novelty of the proposed technique is that it is used to transform coefficients of the watermark biometric image instead of the Pseudo Noise sequences or any other feature extraction technique