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<p>We present StegoMed, a deep learning-based framework intended to facilitate secure transmission of medical images in teleradiology environments. Drawing inspiration from GAN-based representations, StegoMed uses a lightweight two-stage encoder-decoder architecture without a discriminator which enables stable training and computational efficiency. The proposed framework embeds MRI brain images within natural cover images and subsequently reconstructs them with high fidelity. To enhance both imperceptibility and recoverability, the model is optimized by combining pixel-wise reconstruction, perceptual, and structural similarity losses. Experimental results demonstrate that StegoMed achieves high average reconstruction quality (PSNR = 47.7 dB, SSIM = 0.9976, MSE = 0.00006) and outperforms several existing baselines. The method also shows robustness to common distortions such as JPEG compression and Gaussian noise. These results demonstrate StegoMed’s potential as an effective and privacy-preserving solution for secure medical image transmission in modern teleradiology workflows.</p>

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StegoMed: a GAN inspired medical image steganography using non-adversarial encoder decoder for teleradiology

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Bini M Issac,
  • S. N Kumar

摘要

We present StegoMed, a deep learning-based framework intended to facilitate secure transmission of medical images in teleradiology environments. Drawing inspiration from GAN-based representations, StegoMed uses a lightweight two-stage encoder-decoder architecture without a discriminator which enables stable training and computational efficiency. The proposed framework embeds MRI brain images within natural cover images and subsequently reconstructs them with high fidelity. To enhance both imperceptibility and recoverability, the model is optimized by combining pixel-wise reconstruction, perceptual, and structural similarity losses. Experimental results demonstrate that StegoMed achieves high average reconstruction quality (PSNR = 47.7 dB, SSIM = 0.9976, MSE = 0.00006) and outperforms several existing baselines. The method also shows robustness to common distortions such as JPEG compression and Gaussian noise. These results demonstrate StegoMed’s potential as an effective and privacy-preserving solution for secure medical image transmission in modern teleradiology workflows.