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<p>In recent years, the increasing computational and storage demands of deep steganalysis models have drawn attention to lightweight architectures. While pruning algorithms for image steganalysis networks have been proposed, they often do not apply to networks equipped with mobile inverted bottleneck (MBConv) structures, such as EfficientNet. In this paper, we propose a Squeeze-and-Excitation Attention-based Pruning framework for image steganalysis networks, named SEAP. The method adopts a block-wise structured pruning strategy guided by the SE channel attention mechanism, where unimportant channels within each MBConv block are identified based on SE attention values and soft masks. Since pruning is conducted independently within each MBConv block and the input/output dimensions of the block remain unchanged, potential pruning conflicts across blocks are effectively avoided. In addition, we propose a sparsity regularization mechanism that adaptively adjusts the regularization strength based on the network structure, helping to preserve detection performance. Extensive experimental results demonstrate that the pruned network retains only a small fraction of the original network’s parameters and computational costs while achieving performance comparable to the original unpruned networks.</p>

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SEAP: squeeze-and-excitation attention guided pruning for lightweight steganalysis networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月15日

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  • Qiushi Li,
  • Shenghai Luo,
  • Shunquan Tan,
  • Zhenjun Li

摘要

In recent years, the increasing computational and storage demands of deep steganalysis models have drawn attention to lightweight architectures. While pruning algorithms for image steganalysis networks have been proposed, they often do not apply to networks equipped with mobile inverted bottleneck (MBConv) structures, such as EfficientNet. In this paper, we propose a Squeeze-and-Excitation Attention-based Pruning framework for image steganalysis networks, named SEAP. The method adopts a block-wise structured pruning strategy guided by the SE channel attention mechanism, where unimportant channels within each MBConv block are identified based on SE attention values and soft masks. Since pruning is conducted independently within each MBConv block and the input/output dimensions of the block remain unchanged, potential pruning conflicts across blocks are effectively avoided. In addition, we propose a sparsity regularization mechanism that adaptively adjusts the regularization strength based on the network structure, helping to preserve detection performance. Extensive experimental results demonstrate that the pruned network retains only a small fraction of the original network’s parameters and computational costs while achieving performance comparable to the original unpruned networks.