IEWNet: Multi-Scale Robust Watermarking Network Against Infrared Image Enhancement Attacks.

Journal: Journal of imaging
Published Date: May 21, 2025

Abstract

Infrared (IR) images record the temperature radiation distribution of the object being captured. The hue and color difference in the image reflect the caloric and temperature difference, respectively. However, due to the thermal diffusion effect, the target information in IR images can be relatively large and the objects' boundaries are blurred. Therefore, IR images may undergo some image enhancement operations prior to use in relevant application scenarios. Furthermore, Infrared Enhancement (IRE) algorithms have a negative impact on the watermarking information embedded into the IR image in most cases. In this paper, we propose a novel multi-scale robust watermarking model under IRE attack, called IEWNet. This model trains a preprocessing module for extracting image features based on the conventional Undecimated Dual Tree Complex Wavelet Transform (UDTCWT). Furthermore, we consider developing a noise layer with a focus on four deep learning and eight classical attacks, and all of these attacks are based on IRE algorithms. Moreover, we add a noise layer or an enhancement module between the encoder and decoder according to the application scenarios. The results of the imperceptibility experiments on six public datasets prove that the Peak Signal to Noise Ratio (PSNR) is usually higher than 40 dB. The robustness of the algorithms is also better than the existing state-of-the-art image watermarking algorithms used in the performance evaluation comparison.

Authors

  • Yu Bai
    School of Environment, Tsinghua University Beijing 100084 P. R. China zhoulu@mail.tsinghua.edu.cn.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.
  • Shanqing Zhang
    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Jianfeng Lu
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, 518060, China.
  • Ting Luo
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, China. Electronic address: luoting@stu.kust.edu.cn.

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