论文标题
持续学习
Continual Learning for Steganalysis
论文作者
论文摘要
为了检测现有的隐志算法，最近的切解方法通常在数据集中训练卷积神经网络（CNN）模型，该模型由相应的配对盖/stego-Imimages组成。但是，对于那些切断的工具，完全重新训练CNN模型以使其对现有的隐志算法和新的新出现的隐志算法有效。因此，现有的切解模型通常缺乏新的隐志算法的动态扩展性，这限制了其在现实情况下的应用。为了解决这个问题，我们建议基于stemansysis的基于准确的参数重要性估计（APIE）学习方案。在此方案中，当在新的地理算法生成的新图像数据集上训练了训练的模型时，其网络参数将有效，有效地更新，并充分考虑其在先前的培训过程中评估其重要性。这种方法可以指导切解模型来学习新的隐志算法的模式，而不会显着降低针对先前的横向志算法的可检测性。实验结果表明，提出的方案具有新兴新兴志志算法的可扩展性。
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.
