Optimal edge thinning compression and similarity based data hiding for 5G empowered architecture

Optimal edge thinning compression and similarity based data hiding for 5G empowered architecture

연구 분야: Networking

학회: Multimedia Tools and Applications

In today’s world, wireless technology is omnipresent, and millions of people utilize common applications such as iMessage, WhatsApp, and Zoom in their day-to-day lives. However, securely transmitting information can prove to be a challenge. Steganography is a method designed to protect data by concealing it within a cover medium, such as text, image, audio, or video. Factors considered in this process include the quality of the stego image and the hiding capacity, with an emphasis on maintaining a low distortion rate to evade detection by the human visual system. This paper proposes using the 5G-New Radio (NR) or 4G-Long Term Evolution (LTE) architecture for the data hiding process. In a bid to improve the compression rate and thereby enhance hiding capacity, the study introduces an optimal edge thinning algorithm implemented via the Aquila Optimizer (AO), and a similarity-based edge data hiding process. Initially, any noise present in the cover image is eliminated using a bilateral filter. Subsequently, edges are identified and examined for looping or discontinuous features. The AO optimizer then helps identify optimal edges and utilizes the quantization compression method to compress the image. Lastly, a method of identifying similar pixels in the bit planes is adopted, in these similar pixels without altering their values. Non-similar pixels (i.e., edge regions) hide one-bit data to maintain minimal distortion, while non-edge regions utilize Pixel Value Differencing (PVD) within different ranges to ensure the security of the image. The results indicate that the proposed method guarantees good performance and data hiding capacity, while also reducing processing time.