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<p>Reversible Data Hiding (RDH) techniques based on Multiple Histograms Modification often struggle with optimal region classification, encryption robustness and memory efficiency. To address these challenges, this paper proposes a Reversible Data Hiding in Encrypted Images using Similarity-Navigated Graph Neural Networks with Efficient and Stable Thumbnail Preserving Encryption (RDH-EI-SNGNN-ESTPE). The Efficient and Stable Thumbnail Preserving Encryption (ESTPE) mechanism encrypts the image while preserving a low-resolution preview, which enable secure transmission without compromising usability. Then, Similarity-Navigated Graph Neural Networks (SNGNN) optimize embedding locations by efficiently navigating spatial similarities in the encrypted domain, ensuring adaptive and high-capacity data hiding. The encrypted and embedded image is safely transmitted with a thumbnail preview for verification. At the receiver end, the hidden data is extracted and the original image is fully reconstructed without loss. The experimental results on the COCO dataset exhibit a high Peak Signal-to-Noise Ratio of 45.8&#xa0;dB, minimal memory usage of 92.7&#xa0;MB and a high structural similarity of 0.987. The proposed method enhances adaptability and embedding capacity while maintaining encryption security.</p>

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Reversible data hiding in encrypted images using graph neural networks with thumbnail preserving encryption

  • 期刊论文

  • 发表日期：

    2025年6月26日

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  • S. Bharathidasan,
  • V. Tamil Selvi,
  • V. Sathiya,
  • Francis H. Shajin

摘要

Reversible Data Hiding (RDH) techniques based on Multiple Histograms Modification often struggle with optimal region classification, encryption robustness and memory efficiency. To address these challenges, this paper proposes a Reversible Data Hiding in Encrypted Images using Similarity-Navigated Graph Neural Networks with Efficient and Stable Thumbnail Preserving Encryption (RDH-EI-SNGNN-ESTPE). The Efficient and Stable Thumbnail Preserving Encryption (ESTPE) mechanism encrypts the image while preserving a low-resolution preview, which enable secure transmission without compromising usability. Then, Similarity-Navigated Graph Neural Networks (SNGNN) optimize embedding locations by efficiently navigating spatial similarities in the encrypted domain, ensuring adaptive and high-capacity data hiding. The encrypted and embedded image is safely transmitted with a thumbnail preview for verification. At the receiver end, the hidden data is extracted and the original image is fully reconstructed without loss. The experimental results on the COCO dataset exhibit a high Peak Signal-to-Noise Ratio of 45.8 dB, minimal memory usage of 92.7 MB and a high structural similarity of 0.987. The proposed method enhances adaptability and embedding capacity while maintaining encryption security.