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Secure edge-guided adaptive image steganography using HED-based attention maps and CNN.

Rana Alrawashdeh¹, Sultan Almuhammadi^1,2, Mahmood Niazi^1,2

¹Department of Information and Computer Science, College of Computing and Mathematics, King Fahd University of Petroleum and Minerals, 31261, Dhahran, Saudi Arabia.

Scientific Reports

|December 2, 2025

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This summary is machine-generated.

This study introduces a novel image steganography system using deep learning and edge detection for secure data embedding. The method balances capacity and distortion, achieving high imperceptibility and security against steganalysis.

Area of Science:

Computer Science
Information Security
Digital Image Processing

Background:

Image steganography enables covert data transmission by hiding information within cover images.
Existing methods often struggle to balance embedding capacity, image distortion, and security.
Deep learning offers potential for more sophisticated and adaptive steganographic techniques.

Purpose of the Study:

To propose an advanced steganographic system utilizing edge-aware attention mechanisms within deep learning frameworks.
To enhance adaptive data embedding by directing operations based on image features.
To optimize the embedding process for improved capacity, imperceptibility, and security.

Main Methods:

Employing Holistically-Nested Edge Detection (HED) to extract edge maps and convert them into attention maps.

Keywords:

Blowfish Fused Maps Particle Swarm Optimization (PSO)Steganography

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Utilizing attention maps to guide adaptive bit embedding in cover images, adjusting bits per pixel based on attention strength.

Implementing a custom adaptive Least Significant Bits (LSBs) strategy guided by a trained encoder-decoder Convolutional Neural Network (CNN).

Optimizing the embedding rules using a genetic algorithm (GA) to fine-tune attention map thresholds.

Main Results:

The system achieves high Peak Signal to Noise Ratio (PSNR) values (e.g., 60.72-61.20 dB at 0.1 bits per pixel) and Structural Similarity Index (SSIM) values (0.9995-0.9996).
Steganalysis techniques (Xu-Net, Ye-Net, RS analysis) failed to reliably detect hidden data, indicating strong security.
The method demonstrates robustness against salt-and-pepper noise and maintains reasonable performance against other common image attacks.

Conclusions:

The proposed deep learning-based image steganography system effectively balances embedding capacity, imperceptibility, and security.
Edge-aware attention mechanisms significantly improve adaptive data embedding.
The system offers a low computational cost solution with promising resistance to various attacks.