An enhanced adaptive image steganography method using block skin-maps and the integer S-transform

Digital image steganography is the art and science of hiding secret information in an innocent looking cover image to covertly exchange sensitive information in real-world scenarios. This paper presents a transform-domain steganographic method that leverages the Discrete Wavelet Transform (DWT) and a skin-based masking mechanism to identify perceptually less sensitive regions for embedding while maintaining high imperceptibility and extraction accuracy. The proposed method extends our previous work using S-transform which is an integer-to-integer discrete wavelet transform (DWT). The hiding process starts with dividing the cover image into the basic color channels and applying DWT on each channel independently. The approximation coefficients of the DWT are then used to build a blocked skin-map. Only a pixel marked as "skin" in the blocked map will cause its corresponding approximation coefficients to be embedded with the bits of the secret message. Experimental results demonstrate that the proposed approach achieves competitive performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), outperforming several existing methods. Limitations and future directions, including robustness to geometric distortions and steganalysis detection, are discussed.