الفهرس | Only 14 pages are availabe for public view |
Abstract Speech or voice is a biometric candidate that can be used to recognize persons from their spoken words. The main target of Speaker Identification (SI) systems is to distinguish the person from his spoken words. Generally, the security of biometric systems is very important in present-day life. Unprotected biometric templates raise concerns about security and privacy, as for instance, susceptibility to attacks, cumbersome renewability, and cross-matching. So, if these templates are attacked, the biometrics are lost forever. Hence, there is a need to secure original biometrics by keeping them away from utilization in biometric databases. Cancelable biometric schemes provide solutions to achieve privacy preservation in the biometric recognition process. Cancelable biometric systems depend on the transformation of data extracted from original biometrics into a new format, so that users can replace their biometric templates in the same or different systems. This thesis addresses the problem of cancelable SI. It introduces six cancelable SI systems based on spectrogram estimation and different encryption schemes. The first system depends on chaotic Baker map encryption, as it is a permutation tool, which performs the randomization of a square matrix of dimensions N × N by changing the pixel positions based on a secret key, sample unwrapping of elements in subblocks to rows, and then final randomization of the array. The second system depends on Radon transform, which is applied as a second step on the spectrogram of the signal encrypted using Baker map. The Radon transform gives the new templates to be stored in the database. The third system relies on Rivest-Shamir-Adleman (RSA) algorithm for encryption. The RSA based on the principle of multiple keys. The public key is utilized for message encryption, and it can be visible to anyone, while the private key is utilized for message decryption, and it must be kept confidential. Three stages are involved in RSA operation: key generation, encryption and decryption. Abstract iv The fourth system depends on the use of the Radon transform as an additional step after the encryption phase with the aim of increasing the security of the system and making the task difficult for any attacker. After using the RSA technology to encrypt voice signals, we estimate the spectrograms of these encrypted signals, and then apply the Radon transform to the spectrograms representing the new templates that will be stored in the database. The fifth system depends on the 3D chaotic map as a hybrid cryptosystem that combines five stages to complete the overall encryption process for building a reliable and robust cancelable biometric recognition system. The proposed five stages are 3D chaos generation, chaos histogram equalization, row rotation, column rotation, and XOR operation. It is evident from the obtained results that the proposed systems are secure, reliable and practical. They have good encryption and ability to generate cancelable templates. These characteristics lead to good performance. The proposed cancelable speaker identification systems are evaluated under the influence of Additive White Gaussian Noise (AWGN) with different strengths. This makes them more accurate in identifying the users and also more resistant to attack attempts. In addition, security is enhanced through maintaining the confidentiality of the processed data. The last proposed system relies on deep learning technology for cancelable SI. This system introduces three deep pre-trained learning models based on Convolutional Neural Network (CNN) for cancelable SI from spectrograms: AlexNet model, VGG16 model and VGG19 model. The spectrograms are created for speech signals encrypted with Baker map. We treat spectrograms like images, attempting to extract deep characteristics that may be utilized as cancelable templates. Because of the large database, the suggested system uses neural classification rather than threshold-based classification. The introduced cancelable SI systems are evaluated based on probability density function of genuine and imposter distributions. In addition, the Area Under Curve (AUC) is used as a metric for quality of authentication. Simulation tests prove the high quality and security of the proposed cancelable SI systems. |