Blind Embedding Rate Steganalysis Using Refocusing Learning

Existing steganalysis methods perform well under ideal conditions but encounter challenges in real-world scenarios with uncertain embedding rates. This paper proposes a novel steganalysis network based on refocusing learning to enhance detection accuracy for blind embedding rate contexts. The proposed network incorporates a detail gradient guided module (DGGM) to capture subtle spatial changes, which are integrated into multiple layers to ensure the model consistently focuses on these critical details. Additionally, a two-stage training strategy is employed. It is initially trained to obtain a pre-trained model, while the second stage optimizes the pre-trained convolutional kernels by refocusing learning. This approach enhances the feature extraction ability by indirectly strengthening connections between different channels. Experimental results demonstrate that the proposed method achieves strong detection performance across various spatial and JPEG domain steganographic algorithms with blind embedding rates, outperforming SRNet, EfficientNet-B4, and DATNet in detection accuracy.