LAUSR

Some robust data hiding approaches have been proposed to transmit secret data through online social networks (OSNs). Traditional steganalysis tools are inefficient in detecting these tailored steganographic methods. Although some algorithms have been developed to remove the hidden data of images, they are criticized for their low secret removal rate and poor image quality. Moreover, most of them are nongeneric methods, and multiple models must be trained to fit different algorithms. In this letter, we propose a general end-to-end data hiding break approach for OSNs, called the secret data remover (SDR). It is universal for algorithms of both robust steganography and robust watermarking, with which stego images are directly input and clean ones with the same appearances will then be generated. Moreover, we develop two novel techniques, namely, image fusion and latent renewal, to enhance the image quality and improve the overall performance. Experiments show that our proposed method achieves superior performance compared to state-of-the-art works. Hidden secret data are cleared while image quality is maintained or even slightly improved. At the same time, our work can be easily deployed in OSNs.