Abstract:

With the prosperity of Internet technology and the popularity of social networks, reversible data hiding technology has been widely adopted in concealed information transmission of medical and military fields with its advantages on secret information recovery.Traditional reversible data hiding schemes mainly focus on the enhancement of embedding capacity and the reduction of the distortion rate of stego image, but pay less attention to the understanding of image details with the human eyes.Thus, it is difficult to resist hidden information detection methods.To solve the above challenge, a reversible data hiding algorithm was proposed, which ensured the visual quality of the stego image in the process of data hiding through the image visual smoothness enhancement.Specifically, the original image was divided into reference area and non-reference area.The secret data was embedded through the translation of the difference, which was calculated according to the predicted pixel value and the original pixel value of the non-reference area.To guarantee the visual quality of the image, smoothing mechanism was constructed, in which a Gaussian filter was utilized as a template to filter the predicted value and to add the filter difference into the cover image without loss.The pixel value of the reference region was used as edge information for lossless restoration of the original image.The filtering coefficient in Gaussian function was exploited as the embedded key to ensure the security of secret information.Simulation results regarding a large number of classical image data sets illustrated that the visual smoothness of stego image processed by this scheme was effectively enhanced with lower distortion rate, higher embedding rate, and higher embedding and extraction efficiency.In a typical circumstance, the similarity between the generated stego image and the Gaussian filter image can reach 0.9963.The PSNR and the embedded capacity can be up to 37.346 and 0.3289 BPP, respectively.

Key words: reversible data hiding, secret image, Gaussian filter, prediction error