论文标题
美元
$\ell_1$SABMIS: $\ell_1$-minimization and sparse approximation based blind multi-image steganography scheme
论文作者
论文摘要
隐身术通过将其嵌入封面媒体中来实现秘密数据安全方面起着至关重要的作用。封面媒体和秘密数据可以是文本或多媒体，例如图像，视频等。在本文中，我们提出了一种新颖的$ \ ell_1 $ - 最小化和基于稀疏近似的基于盲目的多图像隐志方案，称为$ \ ell_1 $ sabmis。通过使用$ \ ell_1 $ sabmis，可以将多个秘密图像隐藏在单个封面图像中。在$ \ ell_1 $ sabmis中，我们将封面图像采样为四个子图像，使每个子图像块占用范围，然后获得线性测量。接下来，我们获得秘密图像的DCT（离散余弦变换）系数，然后将它们嵌入封面图像\ TextQuotesingle s线性测量结果。 我们对几个标准的灰度图像进行实验，并评估嵌入能力，PSNR（峰值信噪比）值，平均SSIM（结构相似性）指数，NCC（归一化互相关）系数，NAE（归一化的绝对误差）和入口。这些评估指标的价值表明，$ \ ell_1 $ sabmis的表现优于类似的现有隐志方案。也就是说，我们在单个封面图像中成功隐藏了两个以上的秘密图像，而不会显着降低封面图像。此外，提取的秘密图像可保留良好的视觉质量，而$ \ ell_1 $ sabmis对地理攻击具有抵抗力。
Steganography plays a vital role in achieving secret data security by embedding it into cover media. The cover media and the secret data can be text or multimedia, such as images, videos, etc. In this paper, we propose a novel $\ell_1$-minimization and sparse approximation based blind multi-image steganography scheme, termed $\ell_1$SABMIS. By using $\ell_1$SABMIS, multiple secret images can be hidden in a single cover image. In $\ell_1$SABMIS, we sampled cover image into four sub-images, sparsify each sub-image block-wise, and then obtain linear measurements. Next, we obtain DCT (Discrete Cosine Transform) coefficients of the secret images and then embed them into the cover image\textquotesingle s linear measurements. We perform experiments on several standard gray-scale images, and evaluate embedding capacity, PSNR (peak signal-to-noise ratio) value, mean SSIM (structural similarity) index, NCC (normalized cross-correlation) coefficient, NAE (normalized absolute error), and entropy. The value of these assessment metrics indicates that $\ell_1$SABMIS outperforms similar existing steganography schemes. That is, we successfully hide more than two secret images in a single cover image without degrading the cover image significantly. Also, the extracted secret images preserve good visual quality, and $\ell_1$SABMIS is resistant to steganographic attack.
