Steganography in RGB Images Using Adjacent Mean

Yun Hsin Chuang, Bor Shing Lin, Yan Xiang Chen, Hung Jr Shiu

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Steganography is the practice of hiding information or data in a seemingly innocuous cover medium, such as message, file, image, audio, and video. In the past decades, many approaches of steganography in images were proposed for various applications. In social communication and the information highly exposed society, steganography requires high embedding capacity to transmit secret data efficiently. Generally, there is a trade-off between fidelity and embedding capacity. In this paper, we propose a novel and efficient data hiding algorithm in 24-bit color images with super high embedding capacity and acceptable peak signal-to-noise ratio (PSNR) using spatial-domain-adjacent mean. In the proposed algorithm, the embedding rate is about 7.4 bits per pixel (bpp) when the PSNR is nearly 30, and the embedding rate is about 8.88 bpp when the PSNR is nearly 25. The advantage of the proposed method is no need to transform data in another domain and without training data. Experiments also demonstrate the imperceptibility under some state-of-art steganalysis. The proposed steganography provides an efficient way to transmit sensitive information in the information highly exposed society.

Original languageEnglish
Pages (from-to)164256-164274
Number of pages19
JournalIEEE Access
Publication statusPublished - 2021

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering


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