Real-time image carrier generation based on generative adversarial network and fast object detection

TLDR: A unified architecture which combines real-time object detection based on convolutional neural network, local style transfer using generative adversarial network and steganography together to realize real- time carrier image generation is presented.

Abstract: Image steganography aims to conceal the secret information inside another carrier image. And by embedding the information into the carrier image, the carrier image may suffer certain image distortion. Thus, not only the hiding algorithm should be carefully designed, but also the carrier image should be meticulously selected during the hiding process. This paper follows the idea of creating suitable cover images instead of selecting the ones by presenting a unified architecture which combines real-time object detection based on convolutional neural network, local style transfer using generative adversarial network and steganography together to realize real-time carrier image generation. The object in the carrier image is first detected using a fast object detector and then the detected area is reconstructed through a local generative network. The secret message is embedded into the intermediate generated images during the training process in order to generate an image which is suitable as an image carrier. The experimental results show that the reconstructed stego images are nearly indistinguishable to both human eyes and steganalysis tools. Furthermore, the whole carrier image generation process with GPU implementation can achieve around 5 times faster than the regular CPU implementation which meets the requirement of real-time image processing.