A Comparative Study of Data Hiding Techniques for Different Image Categories

Abstract

The study focuses on enhancing the perceptual appearance of shortened pictures that contain concealed content within them. It proposes a two-phase data concealment approach that integrates natural visual abilities to achieve this enhancement. The context in which this study is conducted is in intelligent capitals, where the Internet of Things (IoT) plays a significant role in gathering various sources of visual data for enhanced administration. When transmitting data over a public network, safety concerns are crucial. To address this, the study introduces a unique information concealing procedure for picture decompression using Absolute Moment Block Truncation Codes (AMBTC). AMBTC is utilized as a means to improve bandwidth utilization while satisfying safety requirements. By employing AMBTC, the study aims to maintain the original moments of the image while reducing mean squared errors (MSE) more effectively than with Block Truncation Code (BTC). The study also highlights the importance of being able to restore the original picture after data concealment. However, the amount of recoverable knowledge may be reduced, or additional knowledge might be required for recovery from the stego picture (the image with concealed data) to maintain the ability to revert to the associated stego image. The approach known as Least Significant Bit (LSB) is mentioned, which involves altering the LSB of images to incorporate digital hidden information into the host picture. This technique does not require additional information for restoration and recovery, but it may not fully restore the stego picture to its original state. In summary, this study presents a two phase data concealment approach using AMBTC to enhance the perceptual appearance of shortened pictures containing concealed content. The ability to restore the original picture is emphasized, and the LSB approach is mentioned as a means of digital information concealment.