Extensible Steganalysis via Continual Learning
Extensible Steganalysis via Continual Learning
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DOI：
10.3390/fractalfract6120708
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发表时间：
2022-11
影响因子：
5.4
通讯作者：
Zhili Zhou;Zihao Yin;Ruohan Meng;Fei Peng
中科院分区：
文献类型：
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作者：
Zhili Zhou;Zihao Yin;Ruohan Meng;Fei Peng
为了实现安全通信，隐写术通常通过将秘密信息嵌入到选自自然图像数据集中的图像中来实现，其中分形图像占据了相当大的比例。为了检测现有隐写算法生成的隐写图像，最近的隐写分析模型通常在由成对的封面/隐写图像组成的数据集上训练卷积神经网络（CNN）。然而，对于那些隐写分析模型来说，完全重新训练 CNN 模型以使其能够有效地检测新兴的隐写算法，同时保持检测现有隐写算法的能力是低效且不切实际的。因此，这些隐写分析模型通常缺乏新隐写算法的动态可扩展性，这限制了它们在现实场景中的应用。为了解决这个问题，我们提出了一种基于精确参数重要性估计（APIE）的隐写分析持续学习方案。在该方案中，当隐写分析模型在由新兴隐写算法生成的新图像数据集上进行训练时，其网络参数得到有效且高效的更新，并充分考虑了在先前训练过程中评估的重要性。该方案可以指导隐写分析模型学习新隐写算法的模式，而不会显着降低相对于先前隐写算法的可检测性。实验结果表明，所提出的方案对于新兴的隐写算法具有良好的可扩展性。
To realize secure communication, steganography is usually implemented by embedding secret information into an image selected from a natural image dataset, in which the fractal images have occupied a considerable proportion. To detect those stego-images generated by existing steganographic algorithms, recent steganalysis models usually train a Convolutional Neural Network (CNN) on the dataset consisting of paired cover/stego-images. However, it is inefficient and impractical for those steganalysis models to completely retrain the CNN model to make it effective for detecting a new emerging steganographic algorithm while maintaining the ability to detect the existing steganographic algorithms. Thus, those 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 a new image dataset generated by a new emerging steganographic algorithm, its network parameters are effectively and efficiently updated with sufficient consideration of their importance evaluated in the previous training process. This scheme 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.