We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using ADMM to solve the LASSO formulation of the underlying minimization problem. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together, to achieve the above mentioned competing goals, forms our most novel contribution. We term our scheme SABMIS (Sparse Approximation Blind Multi-Image Steganography). We perform extensive experiments on several standard images. By choosing the size of the secret images to be half of the of cover image, we obtain embedding capacities of 2 bpp (bits per pixel), 4 bpp, 6 bpp, and 8 bpp while embedding one, two, three, and four secret images, respectively. Our focus is on hiding multiple secret images. For the case of hiding two and three secret images, our embedding capacities are higher than all the embedding capacities obtained in the literature until now. For the case of hiding four secret images, although our capacity is slightly lower than one work, we do better on the other two goals; a) very little deterioration in the quality of the stego-images and extracted secret images, and b) inherently and designed-to-be resistant to steganographic attacks. Additionally, we demonstrate that SABMIS executes in few minutes, and show its application on two real-life problems.
翻译:我们把灰色的秘密图像隐藏在灰色的封面图像中,这被认为是一个具有挑战性的摄像问题。 我们的目标是开发一个带有强化嵌入能力的摄像图案,同时保存Stego图像的视觉质量以及提取的秘密图像,并确保Stego图像对摄像攻击具有抗力性。 我们计划的新颖嵌入规则有助于将秘密图像稀释系数隐藏在过度采样的图像稀释系数中, 以错开的方式将图像稀释系数隐藏在覆盖层中。 我们用ADMM 来构建调色图像图案, 以解决LASO的原始最小化问题。 最后, 利用我们嵌入规则的反向, 从已建的Stego图像图像的视觉质量, 并确保这些图案的视觉质量和图像的隐化能力, 我们的图案的图案, 在两个秘密图案中, 我们的隐化能力, 在两个隐藏的图像中, 我们的隐化能力, 在两个隐藏能力中, 在两个隐藏的图像中, 我们的隐藏能力, 在两个隐藏能力中, 在两个隐藏能力中, 显示一个在两个, 在两个隐藏, 预置的 Bix 。