| Abstrakt: |
Information security from intruders has been around since ancient times. A steganography is used to maintain the secrecy of information. A video Steganography is the transmission of a secret message hidden within an ordinary video stream. A video steganography becomes popular due to its massive capability of accommodating higher payload. The embedded frames resist the video compression in raw domain video steganography. In compressed domain video steganography compressed parameters are a better choice for data embedding. In this paper, an efficient Tasmanian Devil Sail Fish Optimization (TDSFO) algorithm is presented for compressed video stream. A video steganography consists of two main phases, embedding phase and extraction phase. In embedding phase, input video acquired from a database is subjected to key frame extraction. The motion estimation of key frames is done to extract the motion vectors of macroblocks. The optimal selection of macro-blocks is carried out using DCNN. The proposed TDSFO algorithm is used to train DCNN. The proposed TDSFO algorithm is devised through the integration of Tasmanian Devil Optimization (TDO) and Sail Fish Optimizer (SFO). A secret image is embedded within motion-vector using 5-Embed approach. Then embedding of bit-stream is done and subsequently, motion compensation is carried out. After embedding, Discrete Cosine Transform (DCT) quantization and entropy coding are performed. The compressed bit-stream is generated by entropy coding. At the extraction phase a compressed bit-stream is decoded. Embedded motion-vector extraction is carried out using 5-Embed approach followed by bit-stream extraction to extract the secret message bit. The input video is extracted effectively at the extraction phase. Implementation of the proposed technique is carried out the Python tool. The performance of the proposed method is analysed using performance metrics, namely Correlation Coefficient (CC) and Peak Signal Noise Ratio (PSNR) and compared with the existing methods to reveal the effectiveness of the proposed method. |
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