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<p>Designing novel and effective distortion functions for spatial image steganography has become increasingly challenging. The controversial pixels prior (CPP) rule mitigates this by fusing existing distortion functions rather than constructing new ones, but it is limited to functions with comparable security performance. We propose G-CPP, a generalized fusion framework based on the grading of controversial pixels. G-CPP assigns embedding priorities according to pixel conflict levels, enabling more effective utilization of high-potential embedding locations. Furthermore, we introduce a customized scheduling strategy for the G-CPP and cost-spreading rules, tailored to the intrinsic properties of different distortion-function combinations, thereby enhancing statistical undetectability against both conventional and deep learning–based steganalyzers. G-CPP preserves the advantages of CPP while extending its applicability to functions with significantly divergent security levels. Experiments on multiple benchmark datasets show that G-CPP consistently outperforms the conventional CPP rule, achieving superior security performance across diverse function combinations.</p>

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Enhancing steganographic security via controversial-pixel grading in multi-distortion function fusion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月31日

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  • Xiaosong Li,
  • Qingliang Liu,
  • Donghua Jiang,
  • Shuguo Yang,
  • Chenzi Yang

摘要

Designing novel and effective distortion functions for spatial image steganography has become increasingly challenging. The controversial pixels prior (CPP) rule mitigates this by fusing existing distortion functions rather than constructing new ones, but it is limited to functions with comparable security performance. We propose G-CPP, a generalized fusion framework based on the grading of controversial pixels. G-CPP assigns embedding priorities according to pixel conflict levels, enabling more effective utilization of high-potential embedding locations. Furthermore, we introduce a customized scheduling strategy for the G-CPP and cost-spreading rules, tailored to the intrinsic properties of different distortion-function combinations, thereby enhancing statistical undetectability against both conventional and deep learning–based steganalyzers. G-CPP preserves the advantages of CPP while extending its applicability to functions with significantly divergent security levels. Experiments on multiple benchmark datasets show that G-CPP consistently outperforms the conventional CPP rule, achieving superior security performance across diverse function combinations.