Deep Reinforcement Learning-Based Few-Shot Image Steganography

Deep learning-based steganography techniques mainly rely on large-scale datasets with sufficient training and testing images, but the concealment performance may degrade in the few-shot-based wireless scenarios, such as vehicular networks. In this paper, we propose a few-shot image steganography framework with deep reinforcement learning (RL), which applies an adaptive generative adversarial network (GAN) to embed secret messages in cover images. This is the first work combining hierarchical deep RL with image steganography. We design an improved three-level hierarchical structure for deep RL, which determines the encoder in the GAN to improve the image quality and similarity, the number of cover images to form a few-shot training dataset, and the secret embedding depth to improve concealment performance and reduce overhead. Experiments are performed on both grayscale and RGB images from BOSSbase and Div2K datasets, demonstrating that the proposed framework surpasses SteganoGAN and HiNet benchmarks with better concealment performance and higher ability against steganalysis.