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Related Experiment Videos

Optimized steganographic embedding guided by snake algorithm and fusion-aware attention maps.

Ahmed Aljughaiman1, Rana Alrawashdeh2

  • 1Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, 31982, Al-Ahsa, Saudi Arabia. aaaljughaiman@kfu.edu.sa.

Scientific Reports
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an attention-guided image steganography method that adaptively embeds data using spatial resolution and histogram equalization. The novel approach achieves high imperceptibility and robustness, outperforming existing techniques in secure data hiding.

Keywords:
Attention mapsFusion-awareImage steganographySnake algorithm

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Area of Science:

  • Computer Science
  • Information Security
  • Digital Image Processing

Background:

  • Image steganography conceals data within images, requiring imperceptibility, capacity, and robustness.
  • Existing methods often lack adaptive embedding and advanced security features.

Purpose of the Study:

  • To propose a novel attention-guided steganography framework for enhanced secure data hiding.
  • To improve embedding capacity and robustness against steganalysis and image processing.

Main Methods:

  • Utilizes Spatial Resolution (SR) and Histogram Equalization (HE) with an improved Snake Optimization Algorithm (SOA).
  • Employs adaptive Least Significant Bit (LSB) substitution, embedding 1-4 bits/pixel based on attention values.
  • Integrates Advanced Encryption Standard Galois Counter Mode (AES-GCM) for payload encryption.

Main Results:

  • Achieves visually imperceptible steganography with high Peak Signal-to-Noise Ratio (PSNR) (54-61 dB) and Structural Similarity Index Measure (SSIM) (>0.99998).
  • Demonstrates high robustness and lossless recovery of hidden data.
  • Shows low detectability by CNN-based steganalysis networks (AUC 0.49-0.53).

Conclusions:

  • The proposed attention-guided steganography method offers superior imperceptibility, capacity, and robustness.
  • The adaptive embedding and AES-GCM encryption provide enhanced security against detection and attacks.
  • This framework represents a significant advancement in secure image steganography.