Reversible data hiding for encrypted images with Kd-tree and entropy coding
Objective Nowadays,many people upload their information to the Internet,but the transmission and storage processes have many security problems.In the early days,researchers used encryption technology to protect the informa-tion,easily attracting the attention of decipherers.Therefore,people began to study how to hide secret information in an image.The secret information is transmitted while avoiding the attention of potential attackers.Therefore,reversible data hiding technology has become one of the hotspots of security research.Reversible data hiding technology can embed secret data through subtle modifications to the original image.After the data are extracted,the image can be restored losslessly.The emergence of cloud storage and big data technology has encouraged many users to upload their images to the cloud server.Out of distrust of the service provider,the image is encrypted before being uploaded to the cloud server.Cloud stor-age service providers hope to embed additional data in images to facilitate image management,image retrieval,copyright protection,and other requirements.Therefore,for cloud applications,reversible data hiding in encrypted images(RDHEI)has attracted the attention of many researchers who hope to embed data in encrypted images for transmission to protect the carrier image effectively and ensure the security of embedded information.The existing RDHEI methods can be divided into two categories depending on whether vacating the space before encrypting the image is necessary:1)vacating the room after encryption and 2)reserving the room before encryption(RRBE).The reversible data hiding technology for encrypted images plays an important role in military,medical,and other aspects.This algorithm can ensure that the con-tent of the carrier is not leaked.It can also transmit secret information.However,most previous methods have problems,such as low data-embedding capacity,errors in data extraction,and poor visual quality of reconstructed images.There-fore,a reversible data hiding algorithm for high-capacity ciphertext images based on Kd-tree and entropy coding is pro-posed to solve these problems.Method Our method needs preprocessing before image encryption.First,the median-edge detector(MED)predictor generates a prediction error absolute value image from the original image,and the prediction error absolute value image is divided into two regions,i.e.,SO region and S1 region.The SO region contains the 5th bit plane to the most significant bit plane,and the Kd-tree algorithm is used to construct the Kd-tree concept subtree,which marks the blocks of the four-bit planes to determine whether the blocks can accommodate secret bits.The S1 region is from the least significant bit plane to the 4th bit plane,and the bitstream of each bit plane is compressed using arithmetic coding.The remaining space can be used to embed the secret data.After the image is encrypted with the encryption key,additional information is embedded to generate the encrypted image.During the stage of secret data embedding,the secret data are embedded according to the additional information and data hiding key to generate the secret image.In the decoding stage,the secret data are extracted,and the image is restored losslessly according to the additional information,encryption key,and data hiding key.Result Experiments show that the proposed method can effectively reduce the number of reference pix-els and additional information,thereby increasing the data embedding rate.The BOWS-2 data set is tested in the experi-ment.The average embedding capacity is 3.909 8 bit/pixel,which is higher than the existing five methods.Two indica-tors,peak signal-to-noise ratio(PSNR)and structural similarity index measure(SSIM),are used to evaluate the similarity between the original and restored images.Experimental results prove that in the data extraction and image restoration stage,the original image does not show a difference after the extraction of the secret data and the use of the image encryp-tion key to decrypt the image.The analysis of the Kd-tree label through encryption shows that texture complexity signifi-cantly impacts the embedding of the image's secret data.The higher the label provided by the relatively smooth image is,the higher the embedding capacity is.Conclusion First,the image pixels are predicted by the predictor.Then,the image pixels are classified and divided into two regions.This method adopts the framework of RRBE.The image must be prepro-cessed before image encryption.It achieves a higher embedding capacity than the related algorithms.It can also perfectly reconstruct the original image and ensure the security of encrypted images and additional data.At present,many disci-plines are combined with deep learning.However,studies combining deep learning with reversible data hiding algorithms in the encrypted domain are lacking.In the future,we hope to achieve breakthroughs in this area and will pay considerable attention to the application of RDHEI in reality,not just in academic research.
image encryptionKd-tree labelMED predictorreversible data hiding(RDH)prediction error
万方数据
维普
