Video Steganography Using Chaos Encryption Algorithm with High Efficiency Video Coding for Data Hiding.

Autor: Vivek, Jaladi, Gadgay, Baswaraj
Předmět:
Zdroj: International Journal of Intelligent Engineering & Systems; 2021, Vol. 14 Issue 5, p15-24, 10p
Abstrakt: Steganography is also known as data hiding, is a method of ensuring the security and confidentiality of digital data by hiding sensitive data in digital media. In video steganography, compression is the most demanding research area in block based video encoders. With the improvement of video coding equipment, the High Efficiency Video Coding (HEVC) delivers better coding efficiency. However, a large proportion of video frames showed system complexity and required a larger Coding Unit (CU) during encoding. Also, as the larger number of video frames were encoded, the time consumption was increased. To overcome such an issue, this research introduced the chaos with enhanced mapping technique to reduce computational complexity and fast encoding. In this research study, the position of each pixel of the secret video frame is calculated by the ELSB technique, where the existing LSB techniques have not considered this secret video frame’s position which leads to high video distortion. In proposed chaos encryption with mapping method, the input video frames are encrypted by using logistic and henon mapping that simultaneously maintains quality and efficiency of the model. The HEVC technique compresses the input video frames and the proposed ELSB performs two functions namely replacement of LSB and matching of LSB which effectively embeds the secret video frames into cover video frames. Finally, the secret video frames are retrieved by HEVC technique with chaos decryption which provides security to the model. The results proved that proposed compression technique achieved PSNR of 35.81dB with average execution time of 40 seconds, but the existing H.264 compression technique achieved PSNR of 32.72dB with average execution time of 45 seconds. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index