Improving VGG-Style Convnet for JPEG Steganalysis

Abstract

The steganalysis of JPEG images is a crucial area of research. Deep-learning based steganalysis methods have achieved superior detection performance. All methods for JPEG steganalysis rely on residual networks. Although the incorporation of residual connections has enhanced detection performance, it has also led to a notable increase in computational complexity. Furthermore, most of these methods are not complete end-to-end models. In their approaches, traditional hand-crafted filters are employed for image preprocessing. To avoid relying on residual connections and prior knowledge, we propose an end-to-end VGG-style ConvNet. During training, the model utilizes a multi-branch architecture, while it is transformed into a VGG-style ConvNet through structural reparameterization during inference. We conduct extensive experiments on ALASKA KAGGLE dataset and ALASKA II dataset, demonstrating that the proposed method achieves state-of-the-art results in the JPEG domain comparable to other CNN-based steganalyzers such as UCNet and EfficientNet, with clearly better convergence capacity and lower model complexity.