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IMAGE STEGANOGRAPHY METHOD BASED ON KOHONEN NEURAL NETWORK

A new high capacity image steganography method based on kohonen neural network is introduced. Kohonen network is trained according to the absolute contrast sensitivity of pixels present in cover image. Trained network classify the pixels in different classes of sensitivity. Data embedding is performed in less sensitive pixels by LSB substitution method, which replaces the least significant bits of cover image with secret information that would be embedded. We implement Optimal Pixel Adjustment Process (OPAP) to obtain an optimal mapping function to reduce the difference error between the cover and the stego-image, therefore improving the hiding capacity with low distortions. On the receiving side, the original image is not needed for extracting the embedded data. It is observed that the capacity and security is increased with acceptable PSNR in the proposed algorithm compared to the existing algorithm.