Steganography in animated emoji using self-reference

Animated emoji is a kind of GIF image, which is widely used in online social networks (OSN) for its efficiency in transmitting vivid and personalized information. Aiming at realizing covert communication in animated emoji, this paper proposes an improved steganography framework in animated emoji. We propose a self-reference algorithm to improve the steganography security. Meanwhile, the relations between adjacent frames of the cover GIF image are considered to further improve the distortion function. After that we embed the secret message into the GIF image using the popular framework of Syndrome Trellis Coding (STC). Experimental results show that the proposed method can provide better security performances than state-of-the-art works.

Performance Analysis of Grouped Multilevel Space-Time Trellis Coding Technique Using Cognitive Radio in Different Deployment Models

In this era, the number of users in a network is increasing tremendously at a faster rate; as a consequence, quality of service (QoS) is drastically deteriorating. To compensate such kinds of problems, we attempted to enhance the QoS of the network, which leads to an improvement in throughput, link quality, spectral efficiency, and many more. To meet the requirements mentioned above, many researchers intervene to advance and propose different techniques with an appropriate design methodology. In this work, we try to emphasize symbol error rate (SER) and frame error rate (FER) by implementing some of the existing space-time coding techniques like Space-Time Trellis Coding (STTC), multilevel space-time trellis coding (MLSTTC), and grouped multilevel space-time trellis coding (GMLSTTC). Though all these techniques are proved to be efficient enough, we explicitly included a powerful method of cooperative diversity-based spectrum sensing in cognitive radio scenario. From this analysis, we landed on to the conclusion that this technique is far better to deal with all these parameters, which can improve the QoS of the network. This paper has also analyzed the effect of the proposed model of GMLSTTC with cognitive radio on various deployment setups such as urban, suburban, and rural macrodeployment setup of the ITU-R M.2135 standard.