College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.
Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, Xinxiang 453007, China.
Sensors (Basel). 2020 Dec 17;20(24):7253. doi: 10.3390/s20247253.
The traditional cover modification steganography method only has low steganography ability. We propose a steganography method based on the convolutional neural network architecture (Xception) of deep separable convolutional layers in order to solve this problem. The Xception architecture is used for image steganography for the first time, which not only increases the width of the network, but also improves the adaptability of network expansion, and adds different receiving fields to carry out multi-scale information in it. By introducing jump connections, we solved the problems of gradient dissipation and gradient descent in the Xception architecture. After cascading the secret image and the mask image, high-quality images can be reconstructed through the network, which greatly improves the speed of steganography. When hiding, only the secret image and the cover image are cascaded, and then the secret image can be embedded in the cover image through the hidden network in order to obtain the secret image. After extraction, the secret image can be reconstructed by bypassing the secret image through the extraction network. The results show that the results that are obtained by our model have high peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), and the average high load capacity is 23.96 bpp (bit per pixel), thus realizing large-capacity image steganography surgery.
传统的封面修改隐写术方法只有较低的隐写能力。为了解决这个问题，我们提出了一种基于深度可分离卷积层的卷积神经网络架构（Xception）的隐写方法。Xception 架构首次用于图像隐写，不仅增加了网络的宽度，而且提高了网络扩展的适应性，并添加了不同的接收场来进行多尺度信息。通过引入跳跃连接，我们解决了 Xception 架构中梯度耗散和梯度下降的问题。通过级联秘密图像和掩模图像，通过网络可以重建高质量的图像，从而大大提高了隐写的速度。在隐藏时，只需级联秘密图像和封面图像，然后通过隐藏网络将秘密图像嵌入封面图像中，以获得秘密图像。提取后，通过提取网络绕过秘密图像，可以重建秘密图像。结果表明，我们的模型得到的结果具有较高的峰值信噪比（PSNR）和结构相似性（SSIM），平均高负载能力为 23.96 bpp（每像素比特），从而实现了大容量图像隐写手术。
