Study on Security of Online Voting System Using Biometrics and Steganography

Abstract: It is very important to provide security to voting system and mostly which is online one. In this paper we provide security to online voting system with secure user authentication by providing biometric as well as password security to voter accounts. Basic idea behind this is to combine secret key with cover image on the basis of key image. As a result such new image is produced by system called stego image which is quite same as cover image. The key image is a biometric measure, such as a fingerprint image. Extraction of stego image is take place at server side to perform the voter authentication. The system minimizes the risk factor as hacker needs to find not only the template but also secret key and it is not possible. It makes election procedure to be secure against a variety of fraudulent behaviors. To improve speed SHA 256 used for hashing is replaced with MD5

Coverless image steganography using morphed face recognition based on convolutional neural network

In recent years, information security has become a prime issue of worldwide concern. To improve the validity and proficiency of the image data hiding approach, a piece of state-of-the-art secret information hiding transmission scheme based on morphed face recognition is proposed. In our proposed data hiding approach, a group of morphed face images is produced from an arranged small-scale face image dataset. Then, a morphed face image which is encoded with a secret message is sent to the receiver. The receiver uses powerful and robust deep learning models to recover the secret message by recognizing the parents of the morphed face images. Furthermore, we design two novel Convolutional Neural Network (CNN) architectures (e.g. MFR-Net V1 and MFR-Net V2) to perform morphed face recognition and achieved the highest accuracy compared with existing networks. Additionally, the experimental results show that the proposed schema has higher retrieval capacity and accuracy and it provides better robustness