A High-Imperceptibility Image Steganography Scheme via Makeup Transfer Network and Multiple Feature Fusion

The existing image steganography will inevitably cause modification traces to the cover image, resulting in the risk of secret information leakage. Therefore, this paper proposes a color image steganography algorithm based on Makeup Transfer Network and multi-scale feature fusion. This paper aims to achieve the embedding of secret image in the process of makeup transfer. Specifi-cally, the secret image is initially mapped into its latent representation, then, it performs multi-scale feature fusion with makeup features to generate a makeup-ed stego image, resulting in the excellent quality of steganographic image and the high imperceptibility of secret information. Moreover, the Information Compensation Network ICN was constructed for deep fine-grained feature fusion, by using the differences between the original and rebuilt secret information as network loss, the information of secret image is comprehensively compensated and its quality is further improved. Experimental results show that the proposed scheme exhibits superior image quality on both the target image and the recovered secret image, thus providing good security.