Güvenilir biyometrik kıyım yöntemi (Trustworthy biometric hashing method)

In this paper, we propose a novel biometric hashing method. We employ a password-generated random projection matrix applied to the face images directly instead of applying to the features extracted from face images and improve the methods in the literature. We aim to preserve privacy while achieving desirable accuracy in a biometric verification system. We do the verification in the hash domain and ensure irreversibility. In addition, we can get a new hash value by only changing the password which ensures cancelable biometrics property. We achieve zero equal error rate (EER) on Carnegie Mellon University face database. Furthermore, we achieve an EER of 0.0061, even if the attackers compromise the password and the random number generator. Besides, we test robustness of the proposed system against possible degradations due to sensor and environment inperfections. The norm of error is below optimum threshold obtained at EER for all distortions

Robust blind and non-blind detection for digital watermarking

Rapid development of Internet has greatly increased the need for creation, storage and distribution of digital multimedia products. This raises, however, security concerns due to digital multimedia products high vulnerability to the illegal copying, distribution, manipulation, and other attacks. To remedy these security issues, in literature, the Digital Watermarking has been developed where the information to be hidden is carried by the watermark signal that is transmitted over the host signal. The capacity of a watermarking system is suffered from much degradation such as channel distortion, filtering, JPEG compression, cropping etc. In addition to those degradations, the host signal interference may even limit the capacity of some systems called blind watermarking systems where host signal is not available to the end-users. To mitigate these sources of errors and increase the capacity of the system, in this thesis, we develop robust detection methods. For this purpose, we devise block normalization based methods for blind watermarking system in Discrete Cosine Transform domain. We also propose the channel reliability estimation based detector for both blind quantization based watermarking system in Discrete Wavelet Transform domain and non-blind watermarking system in Discrete Cosine Transform domain. Simulation results demonstrate that the developed detection methods improve the capacity, bit error rate performance and the robustness of the systems as compared to existing methods against various distortions and attacks

Improved security and privacy preservation for biometric hashing

We address improving verification performance, as well as security and privacy aspects of biohashing methods in this thesis. We propose various methods to increase the verification performance of the random projection based biohashing systems. First, we introduce a new biohashing method based on optimal linear transform which seeks to find a better projection matrix. Second, we propose another biohashing method based on a discriminative projection selection technique that selects the rows of the random projection matrix by using the Fisher criterion. Third, we introduce a new quantization method that attempts to optimize biohashes using the ideas from diversification of error-correcting output codes classifiers. Simulation results show that introduced methods improve the verification performance of biohashing. We consider various security and privacy attack scenarios for biohashing methods. We propose new attack methods based on minimum l1 and l2 norm reconstructions. The results of these attacks show that biohashing is vulnerable to such attacks and better template protection methods are necessary. Therefore, we propose an identity verification system which has new enrollment and authentication protocols based on threshold homomorphic encryption. The system can be used with any biometric modality and feature extraction method whose output templates can be binarized, therefore it is not limited to biohashing. Our analysis shows that the introduced system is robust against most security and privacy attacks conceived in the literature. In addition, a straightforward implementation of its authentication protocol is su ciently fast enough to be used in real applications

Block normalization based blind detectors for spread spectrum watermarking systems

In this paper, novel blind watermark detectors are presented for spread spectrum watermarking systems. These detectors are based on the proposed block normalization method. The block normalization method reduces host signal interference by using the local statistics of each 8ÿ8 discrete cosine transform block of the watermarked image. The simulation results show that the block normalization method considerably improves the bit error rate of the existing correlation, covariance and maximum likelihood estimation detectors

Error-correcting output codes guided quantization for biometric hashing

In this paper, we present a new biometric verification system. The proposed system employs a novel biometric hashing scheme that uses our proposed quantization method. The proposed quantization method is based on error-correcting output codes which are used for classification problems in the literature. We improve the performance of the random projection based biometric hashing scheme proposed by Ngo et al. in the literature [5]. We evaluate the performance of the novel biometric hashing scheme with two use case scenarios including the case where an attacker steals the secret key of a legitimate user. Simulation results demonstrate the superior performance of the proposed scheme

A cancelable biometric hashing for secure biometric verification system

In this paper, we propose a secure and robust biometric hashing method. We use Radon transform, min-max quantizer and Reed-Muller decoder in this method. Our goal is to preserve privacy in a biometric recognition system while achieving the desirable accuracy rate. We do the verification using the hash values and hence increase the security of the system. In addition, we guarantee the irreversibility due to the properties of hashing methods. Moreover, if an attacker compromises the hash value, a new hash value can be reconstructed by changing the password. This ensures that the biometric hash values are cancelable. The simulation results demonstrate the efficiency of the proposed method. We achieve an equal error rate (EER) of 0.00145 on Multi Modal Verification for Teleservices and Security applications (M2VTS) face database. In addition, even in case the attacker compromises the secret key and the random number generator, we achieve an EER of 0.1185

Discriminative projection selection based face image hashing

Face image hashing is an emerging method used in biometric verification systems. In this paper, we propose a novel face image hashing method based on a new technique called discriminative projection selection. We apply the Fisher criterion for selecting the rows of a random projection matrix in a user-dependent fashion. Moreover, another contribution of this paper is to employ a bimodal Gaussian mixture model at the quantization step. Our simulation results on three different databases demonstrate that the proposed method has superior performance in comparison to previously proposed random projection based methods