Detecting covert communication channels in raster images

Digital image steganography is a method for hiding secret messages within everyday Internet communication channels. Such covert communications provide protection for communications and exploit the opportunities available in digital media. Digital image steganography makes the nature and content of a message invisible to other users by taking ordinary internet artefacts and using them as cover objects for the messages. In this paper we demonstrate the capability with raster image files and discuss the challenges of detecting such covert communications. The contribution of the research is community awareness of covert communication capability in digital media and the motivation for including such checks in any investigatory analysis

Information Hiding in Images Using Steganography Techniques

Innovation of technology and having fast Internet make information to distribute over the world easily and economically. This is made people to worry about their privacy and works. Steganography is a technique that prevents unauthorized users to have access to the important data. The steganography and digital watermarking provide methods that users can hide and mix their information within other information that make them difficult to recognize by attackers. In this paper, we review some techniques of steganography and digital watermarking in both spatial and frequency domains. Also we explain types of host documents and we focused on types of images

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