PixelSteganalysis: Pixel-Wise Hidden Information Removal With Low Visual Degradation

Dahuin Jung, Ho Bae, Hyun Soo Choi, Sungroh Yoon

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original images. We formulate a problem and address it by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network. Based on the given information, we remove the hidden secret information at the pixel level. We evaluate our technique by comparing it with conventional steganalysis methods using three public benchmarks. As the decoding method of DL based steganography is approximate (lossy) and is different from the decoding method of conventional steganography, we also introduce a new quantitative metric called the destruction rate (DT). The experimental results demonstrate performance improvements of 10-20% in both the decoded rate and the DT.

Original languageEnglish
Pages (from-to)331-342
Number of pages12
JournalIEEE Transactions on Dependable and Secure Computing
Issue number1
StatePublished - 1 Jan 2023

Bibliographical note

Publisher Copyright:
© 2004-2012 IEEE.


  • Image steganalysis
  • active steganalysis
  • active warden
  • image steganography
  • pixel distribution


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