A new cost function for spatial image steganography based on 2D-SSA and WMF

Guoliang Xie, Jinchang Ren, Stephen Marshall, Huimin Zhao, HuiHui Li

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As an essential tool for secure communications, adaptive steganography aims to communicate secret information with the least security cost. Inspired by the Ranking Priority Profile (RPP), we propose a novel two-step cost function for adaptive steganography in this paper. The RPP mainly includes three rules, i.e. Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. We use the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in designing the two-step cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, we deploy the Spreading rule to smooth the resulting image produced by 2D-SSA with WMF. Extensive experiments have shown the efficacy of the proposed method, which has improved performance over four benchmarking approaches against non-shared selection channel attack. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. Besides, the proposed approach is much faster than other model-based methods.
Original languageEnglish
Number of pages12
JournalIEEE Access
Publication statusAccepted/In press - 11 Feb 2021


  • image steganography
  • feature extraction
  • singular spectrum analysis (SSA)
  • weighted median filtering (WMF)
  • ranking priority profile

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