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Calibration-based Steganalysis for Neural Network Steganography

Published: 28 June 2023 Publication History

Abstract

Recent research has shown that neural network models can be used to steal sensitive data or embed malware. Therefore, steganalysis for neural networks is urgently needed. However, existing neural network steganalysis methods do not perform well under small embedding rates. In addition, because of the large number of parameters, the neural network steganography method under a small embedding rate can embed enough information into the model for malicious purposes. To address this problem, this paper proposes a calibration-based steganalysis method, which fine-tunes the original neural network model without implicit constraints to obtain a reference model, then extracts and fuses statistical moments from the parameter distributions of the original model and its reference model, and finally trains a logistic regressor for detection. Extensive experiments show that the proposed method has superior performance in detecting steganographic neural network models under small embedding rates.

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    cover image ACM Conferences
    IH&MMSec '23: Proceedings of the 2023 ACM Workshop on Information Hiding and Multimedia Security
    June 2023
    190 pages
    ISBN:9798400700545
    DOI:10.1145/3577163
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    Published: 28 June 2023

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    Author Tags

    1. calibration
    2. fine-tuning
    3. neural network steganalysis
    4. small embedding rate

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    Funding Sources

    • Xiaomi Young Scholars Program
    • Natural Science Foundation of China

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    Overall Acceptance Rate 128 of 318 submissions, 40%

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