Translation-based image steganography system utilizing autoencoder and CycleGAN

Abstract

Traditional image steganography involves embedding secret information into a cover image, a process that requires modification of the carrier and potentially leaves detectable marks. This paper proposes a novel method of coverless image steganography based on generative models. Initially, a CycleGAN model is constructed and trained to learn the features of different image domains. Subsequently, an Autoencoder model is trained using two sets of images to achieve a precise one-to-one mapping. Once the models are trained, the autoencoder is used on both the sender and receiver sides to convert the cover image (also known as the stego image) into the secret image and vice versa. The CycleGAN model is then utilized to enhance the visual quality of the images generated by the autoencoder. Experimental results demonstrate that this method not only effectively secures secret information transmission but also improves efficiency and increases the capacity for information hiding compared to similar methods.