Optimized interesting region identification for video steganography using Fractional Grey Wolf Optimization along with multi-objective cost function

A novel approach is presented in this paper for hiding the data using Fractional Grey Wolf Optimization. In this work, the key frames are extracted using the Structural Similarity Index measurement (SSIM). Then, individual key frames are subject to the formation of regions with the help of grid lines. The optimization algorithm is used to select the optimal region based on multi-objective cost function, namely energy, coverage, intensity and kurtosis. Simultaneously, the secret information is pre-processed with encryption to strengthen the security of the proposed method. And, finally, on the key frames Lifting Wavelet Transform (LWT) is applied to accomplish the wavelet coefficient frame to hide the secret information. With the help of metrics such as Peak Signal to Noise Ratio (PSNR), Embedding Capacity and Normalized Correlation (NC) the system performance is measured. Moreover, the proposed method obtained a maximal PSNR of 75.141 dB, and embedding capacity of 70.8% respectively to provide more security and higher imperceptibility for a decent video quality.