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<p>Coverless information hiding has gained increasing attention as a secure alternative to traditional steganography, as it conceals secret data without modifying the cover image, thereby significantly improving resistance to steganalysis. However, most existing coverless techniques require multiple cover images to encode meaningful data, limiting their scalability and practicality in large-scale environments. This paper presents a novel approach that enables the embedding and transmission of secret data using a single cover image. The proposed method combines Analytical Clifford-Fourier-Mellin Transform (ACFMT) features with a deep learning-based bidirectional image-to-image mapping network. The method employs a sub-block overlapping mechanism to extract a large set of unique hash codes, storing them in a precomputed lookup table along with their corresponding block locations. The method utilises the ID of the stego image to generate the ID of an unrelated image, which is transmitted as the final output, ensuring that the cover image remains unaltered both visually and statistically. Experimental results demonstrate that the method achieves a high embedding capacity, supporting up to 227,587 bits per image, strong robustness against various image processing attacks, minimal execution time, and 100% data recovery accuracy under common attack scenarios. These results highlight the method’s potential for secure, efficient, and scalable coverless information hiding in real-world applications.</p>

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A high capacity coverless information hiding based on the analytical clifford Fourier Mellin transform feature and bidirectional mapping networks

  • 期刊论文

  • 发表日期：

    2025年8月7日

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  • Nitin Kanzariya,
  • Dhaval Jadhav,
  • Nilesh Maltare,
  • Lokesh Gagnani

摘要

Coverless information hiding has gained increasing attention as a secure alternative to traditional steganography, as it conceals secret data without modifying the cover image, thereby significantly improving resistance to steganalysis. However, most existing coverless techniques require multiple cover images to encode meaningful data, limiting their scalability and practicality in large-scale environments. This paper presents a novel approach that enables the embedding and transmission of secret data using a single cover image. The proposed method combines Analytical Clifford-Fourier-Mellin Transform (ACFMT) features with a deep learning-based bidirectional image-to-image mapping network. The method employs a sub-block overlapping mechanism to extract a large set of unique hash codes, storing them in a precomputed lookup table along with their corresponding block locations. The method utilises the ID of the stego image to generate the ID of an unrelated image, which is transmitted as the final output, ensuring that the cover image remains unaltered both visually and statistically. Experimental results demonstrate that the method achieves a high embedding capacity, supporting up to 227,587 bits per image, strong robustness against various image processing attacks, minimal execution time, and 100% data recovery accuracy under common attack scenarios. These results highlight the method’s potential for secure, efficient, and scalable coverless information hiding in real-world applications.