Cover Selection in Encrypted Images

Abstract

Existing effective cover selection methods aim to select the complex images as covers to achieve the highly security with the aid of the embedding distortion computed from a natural image. However, the calculation of the embedding distortion divulges the image content to a steganographer. To overcome this issue, this work proposes a novel cover selection scheme in encrypted images to achieve the image content-protection and cover-selection simultaneously. In the first phase, the content owner encrypts several most significant bits (MSBs) of each image using an encryption key and the encrypted image is shuffled by block. Meanwhile, with a sampling key, the content owner selects some encrypted blocks and outputs them to the steganographer. In the second phase, the steganographer calculates first-order noise residuals of adjacent pixels of the acquired blocks along different directions. Importantly, we design a texture descriptor named as structured Local binary pattern (SLBP) to encode all the residuals by which the images owing the maximal SLBP values are chosen as the optimal covers. We demonstrate the security of our proposed scheme on multiple steganographic and steganalytic methods and the extensive results show that our scheme exhibits excellent performance without knowing of the original image content. Moreover, the results testify that the designed SLBP achieves the perfect evaluation of image complexity.