Continual Learning for Steganalysis

TL;DR

This paper introduces a continual learning approach for steganalysis that efficiently updates CNN models to detect new steganographic algorithms without forgetting previous ones, enhancing real-world applicability.

Contribution

The paper proposes an accurate parameter importance estimation (APIE) scheme enabling continual learning in steganalysis models, allowing dynamic extension to new algorithms.

Findings

Effective detection of new steganographic algorithms.

Maintains performance on previously learned algorithms.

Promising extensibility demonstrated through experiments.

Abstract

To detect the existing steganographic algorithms, recent steganalysis methods usually train a Convolutional Neural Network (CNN) model on the dataset consisting of corresponding paired cover/stego-images. However, it is inefficient and impractical for those steganalysis tools to completely retrain the CNN model to make it effective against both the existing steganographic algorithms and a new emerging steganographic algorithm. Thus, existing steganalysis models usually lack dynamic extensibility for new steganographic algorithms, which limits their application in real-world scenarios. To address this issue, we propose an accurate parameter importance estimation (APIE) based-continual learning scheme for steganalysis. In this scheme, when a steganalysis model is trained on the new image dataset generated by the new steganographic algorithm, its network parameters are effectively and efficiently updated with sufficient consideration of their importance evaluated in the previous training process. This approach can guide the steganalysis model to learn the patterns of the new steganographic algorithm without significantly degrading the detectability against the previous steganographic algorithms. Experimental results demonstrate the proposed scheme has promising extensibility for new emerging steganographic algorithms.