Reversible image hiding with super-resolution behavior camouflage effect
Objective Image hiding has recently become a hotspot in the computer vision field.It aims to hide the secret image in a cover image imperceptibly and recover the secret image,preferably at the receiver.Traditional image hiding methods often adjust the cover image's pixel value in the spatial domain or modify the cover image's frequency coefficients to hide the secret information.These methods hide secret images through handcrafted feature information.Thus,these secret images can be detected easily by existing detection techniques.These methods have weak security and lack signifi-cant capabilities for hiding information in images.Therefore,they fall short of meeting the demands of large-capacity image hiding tasks.Image hiding methods based on deep learning have been quickly developed with the advancements in convolu-tional neural networks.These deep learning methods seek to achieve a high level of capacity,invisibility,and recovery accuracy.However,the existing image hiding techniques can be easily detected by deep learning analysis methods because of the rapid development of steganalysis.Handcrafted image hiding methods and image hiding methods based on deep learning are camouflaged from the content level to pursue the indistinguishability of the marked image and the cover image.The essence of image hiding is the pursuit of behavioral security;that is,the pursuit of hiding secret information is insepa-rable from the behavior of normal users to achieve good detection resistance.Therefore,we can camouflage at the content level and disguise at the behavior level.We innovatively use super-resolution,a common image processing technology,as our behavior camouflage means to realize image hiding from the behavior security perspective.Method In general,tradi-tional image hiding techniques tend to prioritize the indistinguishability of the cover image and the secret image at the con-tent level.However,we aim to achieve image hiding from a behavioral security perspective in our study.In particular,we aim to make steganographic behavior indistinguishable from regular super-resolution image processing behavior.The entire method can be divided into three modules:forward hiding,super-resolution behavior camouflage,and backward revealing.In the first module,a cover image and a secret image are inputted to the forward hiding module,resulting in a marked image that looks identical to the cover image but with hidden information and information that is lost during forward hiding.The second module involves a lightweight super-resolution rescaling network to realize behavior camouflage.Instead of using traditional convolution for upsampling and downsampling,bicubic interpolation is used.Moreover,we use a pre-trained Visual Geometry Group-19(VGG19)network to extract high-level features and guide the generation of super-resolution behavior camouflage images.The final module is backward revealing.The marked image is first reconstructed using the reversibility of the behavior camouflage module.Then,the reconstructed image and auxiliary matrix are inputted into the backward revealing module to recover the secret image and the cover image.Result Experiments are conducted on recovering secret images,camouflage effect of super-resolution behavior,parameter setting,and ablation.Results show that the invisibility,hiding capacity,and recovery accuracy of our method reach a good level.Only the recovery accuracy of our method is slightly lower than that of the current state-of-the-art(SOTA).In particular,the peak signal-to-noise ratio(PSNR)between the secret image and the recovered secret image using our method can reach 47.23 dB,which is approxi-mately 0.92 dB less than that using SOTA.Moreover,the structural similarity index measure of our method can reach 0.993 8,which is 0.003 4 less than that of SOTA.However,compared with other methods besides SOTA,our method has advantages.Our method's super-resolution behavior camouflage reaches a satisfactory level.Given the reversibility con-straints,we do not look for top-of-the-line super-resolution effects.Our super-resolution behavior camouflage images have a PSNR of 27.43 dB and a perceptual index of 4.568 4,indicating that they reach the satisfaction of human eyes,whether subjectively or objectively.In addition to the above two main indicators,exploratory experiments on the selection of superpa-rameters,module architecture,and loss function are conducted to find the optimal setting and achieve a good combination effect.Conclusion This study proposes a new idea of image hiding in which the process of hiding the secret image,namely,super-resolution processing,is performed simultaneously to obtain a super-resolution behavioral camouflage image with secret information.Thus,the attention of unauthorized parties is diverted,and the protection of the secret image is realized.The experimental results show that our method can achieve high capacity,high invisibility,and high recovery accuracy and plays a good role in the confusion of unauthorized parties.Finally,camouflage images still maintain good visual effects.
万方数据
维普
