The purpose of image steganalysis is to determine whether the carrier image contains hidden information or not. Since JEPG is the most commonly used image format over social networks, steganalysis in JPEG images is also the most urgently needed to be explored. However, in order to detect whether secret information is hidden within JEPG images, the majority of existing algorithms are designed in conjunction with the popular computer vision related networks, without considering the key characteristics appeared in image steganalysis. It is crucial that the steganographic signal, as an extremely weak signal, can be enhanced during its representation learning process. Motivated by this insight, in this paper, we introduce a novel representation learning algorithm for JPEG steganalysis that is mainly consisting of a graph attention learning module and a feature enhancement module. The graph attention learning module is designed to avoid global feature loss caused by the local feature learning of convolutional neural network and reliance on depth stacking to extend the perceptual domain. The feature enhancement module is applied to prevent the stacking of convolutional layers from weakening the steganographic information. In addition, pretraining as a way to initialize the network weights with a large-scale dataset is utilized to enhance the ability of the network to extract discriminative features. We advocate pretraining with ALASKA2 for the model trained with BOSSBase+BOWS2. The experimental results indicate that the proposed algorithm outperforms previous arts in terms of detection accuracy, which has verified the superiority and applicability of the proposed work.

翻译：图像隐写分析的目的在于判定载体图像是否包含隐藏信息。由于JPEG是社交网络中最常用的图像格式，针对JPEG图像的隐写分析也是最迫切需要探索的。然而，现有的大多数算法为了检测JPEG图像中是否隐藏秘密信息，常结合流行的计算机视觉相关网络设计，却未充分考虑图像隐写分析中的关键特性。关键在于，隐写信号作为一种极弱信号，在其表征学习过程中应得到增强。受此启发，本文提出了一种新颖的JPEG隐写分析表征学习算法，主要由图注意力学习模块和特征增强模块构成。图注意力学习模块旨在避免卷积神经网络局部特征学习及依赖深度堆叠扩展感受野所导致的全局特征损失；特征增强模块则用于防止卷积层堆叠削弱隐写信息。此外，我们利用大规模数据集进行预训练以初始化网络权重，增强网络提取判别性特征的能力。对于基于BOSSBase+BOWS2训练模型，我们提倡采用ALASKA2进行预训练。实验结果表明，所提算法在检测精度上优于以往方法，验证了本工作的优越性与适用性。