Enhanced Least Significant Bit Replacement Algorithm in Spatial Domain of Steganography Using Character Sequence Optimization

• Main Authors: Jagan Raj Jayapandiyan, C. Kavitha, K. Sakthivel
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Image steganography; information hiding; secret text sharing; enhanced least significant bit steganography
• Online Access: https://ieeexplore.ieee.org/document/9139933/

Text Steganography has become a dominant research field in information sharing domain and many researches are being conducted to strengthen this area. Researches around the amount of secret message that could be stored in a given cover image is always critical for any steganography technique used to share the secret text. This research paper proposes an enhanced Least Significant Bit (eLSB) embedding technique in steganography, through which the quality of cover image is improved, when compared to typical LSB algorithm used in steganography. The proposed method employs in spatial domain and it does the secret message encoding in two phases. The first phase generates the metadata and embeds the header information in first few bytes of cover image and then the following phase takes care of processing secret message and storing the secret message in cover image using an optimized way, which is possible through analyzing secret text's character sequences. Proposed work results into occupying lesser space for the given secret text in cover image and hence leads to the better stego image quality than existing LSB algorithms. As the algorithm works on optimizing secret message during embedding phase itself, this technique enables high capacity embedding rate, additional security due to secret message preprocessing and enhanced cover image quality. The results are compared with LSB algorithm and compared to Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Root Mean Square Error (RMSE) values to prove the proposed algorithm performs better on secret text embedding in cover image.