基于ResNet架构的深度卷积自编码器彩色图像隐写术
Color Image steganography using Deep convolutional Autoencoders based on ResNet architecture
AI总结：
本文提出一种基于ResNet架构的深度卷积自编码器彩色图像隐写方案，利用反向ResNet提取秘密图像，在COCO和CelebA数据集上取得PSNR超过40 dB、SSIM超过0.98及每像素8比特的隐藏容量，优于传统和以往深度隐写方法。
摘要：
本文提出了一种结合卷积自编码器和ResNet架构的深度学习彩色图像隐写方案。传统隐写方法存在容量低、安全性差和鲁棒性不足等关键缺陷。近几十年来，图像隐藏和图像提取通过自编码器卷积神经网络实现，以解决上述挑战。本文的贡献在于引入了一种受ResNet架构启发的新型彩色图像隐写方案。利用反向ResNet架构从隐写图像中提取秘密图像。在所提出的方法中，所有图像都经过预处理模型，该模型是一个以特征提取为目的的卷积深度神经网络。然后，操作模型生成隐写图像和提取图像。实际上，操作模型是一个基于ResNet结构的自编码器，它从特征图生成图像。所提出结构的优势在于嵌入和提取阶段模型的一致性。使用COCO和CelebA数据集研究了所提出方法的性能。为了与先前相关工作进行定量比较，评估了峰值信噪比（PSNR）、结构相似性指数（SSIM）和隐藏容量。实验结果验证了所提出的方案优于传统和以往的深度隐写方法。PSNR和SSIM分别超过40 dB和0.98，这表明所提出的方法具有很高的不可感知性。此外，该方法可以在另一幅彩色图像中隐藏相同尺寸的彩色图像，由此可以推断所提出方法的相对容量为每像素8比特。
英文摘要：
In this paper, a deep learning color image steganography scheme combining convolutional autoencoders and ResNet architecture is proposed. Traditional steganography methods suffer from some critical defects such as low capacity, security, and robustness. In recent decades, image hiding and image extraction were realized by autoencoder convolutional neural networks to solve the aforementioned challenges. The contribution of this paper is introducing a new scheme for color image steganography inspired by ResNet architecture. The reverse ResNet architecture is utilized to extract the secret image from the stego image. In the proposed method, all images are passed through the prepossess model which is a convolutional deep neural network with the aim of feature extraction. Then, the operational model generates stego and extracted images. In fact, the operational model is an autoencoder based on ResNet structure that produces an image from feature maps. The advantage of proposed structure is identity of models in embedding and extraction phases. The performance of the proposed method is studied using COCO and CelebA datasets. For quantitative comparisons with previous related works, peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM) and hiding capacity are evaluated. The experimental results verify that the proposed scheme performs better than traditional and pervious deep steganography methods. The PSNR and SSIM are more than 40 dB and 0.98, respectively that implies high imperceptibility of the proposed method. Also, this method can hide a color image of the same size in another color image, which can be inferred that the relative capacity of the proposed method is 8 bits per pixel.
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