Cover selection technique for secure transform domain image steganography

In image steganography, an appropriate cover selection offers the least detectable stego image thereby assuring the security of covert communication. In this paper, a new framework is proposed for the optimal choice of cover from the image database based on statistical texture analysis. Texture analysis using a gray level co-occurrence matrix (GLCM) and run-length matrix (RLM) helps to identify the heterogeneity of images. Textural features were extracted and a non-linear support vector machine was employed to classify a suitable cover from image database. This can further be used as a host image to carry the secret message for the image steganography scheme. To justify the validity of the proposed cover selection technique, image steganography algorithm based on double density dual tree DWT (DDDTDWT) and LU decomposition is also proposed in this work. Performance measures like imperceptibility, robustness, and steganalyser’s inability to detect the stego image were employed to check validity of the proposed schemes. Better imperceptibility, strong robustness to stego attacks, and poor detection accuracy by steganalyser confirms the efficacy of proposed cover selection and transform domain image steganography techniques.