School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
Zhe-Jiang Shangfeng Special Blower Company Ltd., Shaoxing 312352, China.
Sensors (Basel). 2023 Jun 6;23(12):5360. doi: 10.3390/s23125360.
Infrared images have been widely used in many research areas, such as target detection and scene monitoring. Therefore, the copyright protection of infrared images is very important. In order to accomplish the goal of image-copyright protection, a large number of image-steganography algorithms have been studied in the last two decades. Most of the existing image-steganography algorithms hide information based on the prediction error of pixels. Consequently, reducing the prediction error of pixels is very important for steganography algorithms. In this paper, we propose a novel framework SSCNNP: a Convolutional Neural-Network Predictor (CNNP) based on Smooth-Wavelet Transform (SWT) and Squeeze-Excitation (SE) attention for infrared image prediction, which combines Convolutional Neural Network (CNN) with SWT. Firstly, the Super-Resolution Convolutional Neural Network (SRCNN) and SWT are used for preprocessing half of the input infrared image. Then, CNNP is applied to predict the other half of the infrared image. To improve the prediction accuracy of CNNP, an attention mechanism is added to the proposed model. The experimental results demonstrate that the proposed algorithm reduces the prediction error of the pixels due to full utilization of the features around the pixel in both the spatial and the frequency domain. Moreover, the proposed model does not require either expensive equipment or a large amount of storage space during the training process. Experimental results show that the proposed algorithm had good performances in terms of imperceptibility and watermarking capacity compared with advanced steganography algorithms. The proposed algorithm improved the PSNR by 0.17 on average with the same watermark capacity.
红外图像在目标检测和场景监控等许多研究领域得到了广泛的应用。因此，红外图像的版权保护非常重要。为了实现图像版权保护的目标，在过去的二十年中，研究人员研究了大量的图像隐写算法。大多数现有的图像隐写算法都是基于像素的预测误差来隐藏信息的。因此，减少像素的预测误差对于隐写算法非常重要。本文提出了一种新颖的框架 SSCNNP：基于平滑小波变换（SWT）和挤压激励（SE）注意力的卷积神经网络预测器（CNN），用于红外图像预测，它结合了卷积神经网络（CNN）和 SWT。首先，使用超分辨率卷积神经网络（SRCNN）和 SWT 对输入红外图像的一半进行预处理。然后，将 CNNP 应用于预测红外图像的另一半。为了提高 CNNP 的预测精度，在提出的模型中添加了注意力机制。实验结果表明，由于充分利用了像素周围的空间和频率域的特征，该算法降低了像素的预测误差。此外，该模型在训练过程中不需要昂贵的设备或大量的存储空间。实验结果表明，与先进的隐写算法相比，所提出的算法在不可感知性和水印容量方面具有较好的性能。该算法在保持相同水印容量的情况下，平均 PSNR 提高了 0.17。
