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Updated: Jul 24, 2025

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A Novel Steganography Method for Infrared Image Based on Smooth Wavelet Transform and Convolutional Neural Network.

Yu Bai1, Li Li1, Jianfeng Lu1

  • 1School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary

This study introduces a novel framework for infrared image copyright protection using a Convolutional Neural-Network Predictor (CNNP) with Smooth-Wavelet Transform (SWT) and Squeeze-Excitation (SE) attention. The method effectively reduces pixel prediction error, enhancing steganography performance.

Keywords:
CNN-based predictorCNNPSRCNNSWTconvolutional neural networkinfrared imagessteganography

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Area of Science:

  • Computer Vision
  • Digital Image Processing
  • Information Security

Background:

  • Infrared images are crucial for target detection and scene monitoring, necessitating robust copyright protection.
  • Existing image steganography algorithms often rely on pixel prediction error, making error reduction vital for effectiveness.

Purpose of the Study:

  • To propose a novel framework, SSCNNP, for infrared image copyright protection.
  • To enhance steganography by improving prediction accuracy in infrared images.
  • To develop a computationally efficient model for infrared image steganography.

Main Methods:

  • A Convolutional Neural-Network Predictor (CNNP) framework, SSCNNP, was developed, integrating Convolutional Neural Network (CNN) with Smooth-Wavelet Transform (SWT).
  • Super-Resolution Convolutional Neural Network (SRCNN) and SWT were used for preprocessing.
  • An attention mechanism, specifically Squeeze-Excitation (SE) attention, was incorporated to boost prediction accuracy.

Main Results:

  • The proposed algorithm significantly reduces pixel prediction error by leveraging spatial and frequency domain features.
  • Experimental results show improved imperceptibility and watermarking capacity compared to existing methods.
  • The algorithm achieved an average PSNR improvement of 0.17 with identical watermark capacity.

Conclusions:

  • SSCNNP offers an effective solution for infrared image copyright protection.
  • The model demonstrates high performance without requiring expensive hardware or extensive storage.
  • The integration of CNN, SWT, and SE attention provides a powerful approach for advanced steganography.