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

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

Abstract 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 bot...

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Bibliographic Details
Main Authors: Qiushi Li, Shenghai Luo, Shunquan Tan, Zhenjun Li
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:EURASIP Journal on Information Security
Subjects:
Steganalysis
Steganography
Deep learning
Convolutional neural network
Network pruning
Online Access:https://doi.org/10.1186/s13635-025-00212-8
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https://doi.org/10.1186/s13635-025-00212-8

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