Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Ciphertext-Policy Attribute-Based Encryption

With the rapid advancement of cloud computing technology,reversible data hiding algorithms in encrypted images(RDH-EI)have developed into an important field of study concentrated on safeguarding privacy in distributed cloud environments.However,existing algorithms often suffer from low embedding capacities and are inadequate for complex data access scenarios.To address these challenges,this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection(AMED)and ciphertext-policy attributebased encryption(CP-ABE).This proposed algorithm enhances the conventional median edge detection(MED)by incorporating dynamic variables to improve pixel prediction accuracy.The carrier image is subsequently reconstructed using the Huffman coding technique.Encrypted image generation is then achieved by encrypting the image based on system user attributes and data access rights,with the hierarchical embedding of the group’s secret data seamlessly integrated during the encryption process using the CP-ABE scheme.Ultimately,the encrypted image is transmitted to the data hider,enabling independent embedding of the secret data and resulting in the creation of the marked encrypted image.This approach allows only the receiver to extract the authorized group’s secret data,thereby enabling fine-grained,controlled access.Test results indicate that,in contrast to current algorithms,the method introduced here considerably improves the embedding rate while preserving lossless image recovery.Specifically,the average maximum embedding rates for the(3,4)-threshold and(6,6)-threshold schemes reach 5.7853 bits per pixel(bpp)and 7.7781 bpp,respectively,across the BOSSbase,BOW-2,and USD databases.Furthermore,the algorithm facilitates permission-granting and joint-decryption capabilities.Additionally,this paper conducts a comprehensive examination of the algorithm’s robustness using metrics such as image correlation,information entropy,and number of pixel change rate(NPCR),confirming its high level of security.Overall,the algorithm can be applied in a multi-user and multi-level cloud service environment to realize the secure storage of carrier images and secret data.

Reversible Data Hiding in Encrypted Image Based on Block Classification Permutation

Recently,reversible data hiding in encrypted image(RDHEI)has attracted extensive attention,which can be used in secure cloud computing and privacy protection effectively.In this paper,a novel RDHEI scheme based on block classification and permutation is proposed.Content owner first divides original image into non-overlapping blocks and then set a threshold to classify these blocks into smooth and non-smooth blocks respectively.After block classification,content owner utilizes a specific encryption method,including stream cipher encryption and block permutation to protect image content securely.For the encrypted image,data hider embeds additional secret information in the most significant bits(MSB)of the encrypted pixels in smooth blocks and the final marked image can be obtained.At the receiver side,secret data will be extracted correctly with data-hiding key.When receiver only has encryption key,after stream cipher decryption,block scrambling decryption and MSB error prediction with threshold,decrypted image will be achieved.When data hiding key and encryption key are both obtained,receiver can find the smooth and non-smooth blocks correctly and MSB in smooth blocks will be predicted correctly,hence,receiver can recover marked image losslessly.Experimental results demonstrate that our scheme can achieve better rate-distortion performance than some of state-of-the-art schemes.

Reversible Data Hiding in Classification-Scrambling Encrypted-Image Based on Iterative Recovery

To improve the security and quality of decrypted images,this work proposes a reversible data hiding in encrypted image based on iterative recovery.The encrypted image is firstly generated by the pixel classification scrambling and bit-wise exclusive-OR(XOR),which improves the security of encrypted images.And then,a pixel-typemark generation method based on block-compression is designed to reduce the extra burden of key management and transfer.At last,an iterative recovery strategy is proposed to optimize the marked decrypted image,which allows the original image to be obtained only using the encryption key.The proposed reversible data hiding scheme in encrypted image is not vulnerable to the ciphertext-only attack due to the fact that the XOR-encrypted pixels are scrambled in the corresponding encrypted image.Experimental results demonstrate that the decrypted images obtained by the proposed method are the same as the original ones,and the maximum embedding rate of proposed method is higher than the previously reported reversible data hiding methods in encrypted image.

A Reversible Data Hiding Algorithm Based on Image Camouflage and Bit-Plane Compression

Reversible data hiding in encrypted image(RDHEI)is a widely used technique for privacy protection,which has been developed in many applications that require high confidentiality,authentication and integrity.Proposed RDHEI methods do not allow high embedding rate while ensuring losslessly recover the original image.Moreover,the ciphertext form of encrypted image in RDHEI framework is easy to cause the attention of attackers.This paper proposes a reversible data hiding algorithm based on image camouflage encryption and bit plane compression.A camouflage encryption algorithm is used to transform a secret image into another meaningful target image,which can cover both secret image and encryption behavior based on“plaintext to plaintext”transformation.An edge optimization method based on prediction algorithm is designed to improve the image camouflage encryption quality.The reversible data hiding based bit-plane level compression,which can improve the redundancy of the bit plane by Gray coding,is used to embed watermark in the camouflage image.The experimental results also show the superior performance of the method in terms of embedding capacity and image quality.

Reversible data hiding in encrypted images based on additive secret sharing and additive joint coding using an intelligent predictor

Reversible data hiding in encrypted images(RDHEI)is essential for safeguarding sensitive information within the encrypted domain.In this study,we propose an intelligent pixel predictor based on a residual group block and a spatial attention module,showing superior pixel prediction performance compared to existing predictors.Additionally,we introduce an adaptive joint coding method that leverages bit-plane characteristics and intra-block pixel correlations to maximize embedding space,outperforming single coding approaches.The image owner employs the presented intelligent predictor to forecast the original image,followed by encryption through additive secret sharing before conveying the encrypted image to data hiders.Subsequently,data hiders encrypt secret data and embed them within the encrypted image before transmitting the image to the receiver.The receiver can extract secret data and recover the original image losslessly,with the processes of data extraction and image recovery being separable.Our innovative approach combines an intelligent predictor with additive secret sharing,achieving reversible data embedding and extraction while ensuring security and lossless recovery.Experimental results demonstrate that the predictor performs well and has a substantial embedding capacity.For the Lena image,the number of prediction errors within the range of[-5,5]is as high as 242500 and our predictor achieves an embedding capacity of 4.39 bpp.

A Steganography Based on Optimal Multi-Threshold Block Labeling

Hiding secret data in digital images is one of the major researchfields in information security.Recently,reversible data hiding in encrypted images has attracted extensive attention due to the emergence of cloud services.This paper proposes a novel reversible data hiding method in encrypted images based on an optimal multi-threshold block labeling technique(OMTBL-RDHEI).In our scheme,the content owner encrypts the cover image with block permutation,pixel permutation,and stream cipher,which preserve the in-block correlation of pixel values.After uploading to the cloud service,the data hider applies the prediction error rearrangement(PER),the optimal threshold selection(OTS),and the multi-threshold labeling(MTL)methods to obtain a compressed version of the encrypted image and embed secret data into the vacated room.The receiver can extract the secret,restore the cover image,or do both according to his/her granted authority.The proposed MTL labels blocks of the encrypted image with a list of threshold values which is optimized with OTS based on the features of the current image.Experimental results show that labeling image blocks with the optimized threshold list can efficiently enlarge the amount of vacated room and thus improve the embedding capacity of an encrypted cover image.Security level of the proposed scheme is analyzed and the embedding capacity is compared with state-of-the-art schemes.Both are concluded with satisfactory performance.

High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction

To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)prediction.First,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images.In the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding space.To the best of our knowledge,quadtree partitioning is first applied to RDH-EI.Simulation results show that the proposed method is reversible and separable,and that its average embedding capacity is improved.For gray images with a size of 512×512,the average embedding capacity is increased by 25565 bits.For all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.

基于邻接差值与块分类的密文域可逆信息隐藏

针对密文域可逆信息隐藏(reversible data hiding in encrypted images,RDHEI)算法存在像素利用率低和嵌入容量小等问题,本文提出了一种基于邻接差值和块分类(adjacency difference and block classification,ADBC)的RDHEI算法。首先,充分利用原始图像的空间相关性,通过计算得到邻接差值图像,根据图像块的最大值实现初次块分类操作;其次,对初次分类中不可嵌入的图像块,采用中值边缘预测器来预测像素,完成第二次块分类;然后,执行图像加密;最后,通过位替换,将辅助信息和秘密信息嵌入加密图像。实验结果表明,本文算法相较于现有算法,在BOSSbase、BOWS-2和UCID 3个数据集上的平均嵌入率(embedding rate,ER)分别提高0.06 bpp、0.01 bpp和0.15 bpp以上,能够获得较高的嵌入容量。

基于Piecewise映射的安全密文域可逆信息隐藏算法

密文域可逆信息隐藏(RDH-ED)中常采用对称加密,但仅采用流密码异或(XOR)、置乱或两者结合的方法,在现有条件下难以抵抗唯密文攻击、选择明文攻击或已知明文攻击(KPA)。为了提高RDH-ED的安全性,提出一种基于Piecewise混沌映射的安全RDH-ED加密算法。首先对明文图像进行分块并按比特异或,随后利用Piecewise混沌映射转换密钥来对异或后的图像进行块内位平面置乱,最后对图像进行块间置乱加密。实验结果表明:所提算法在能够达到像素值均匀分布、视觉上不可感知的基础上,密钥空间相比单一加密方式增大至2^(8N_(p))×N_(p)!×8!^(N_(p)),并能够抵抗现有的各类攻击方法;最近的KPA测试下破解率为0.0067%,在保留适当嵌入量的同时增强了图像的安全性。同时,该算法通过Piecewise映射使得密钥通信量不变,并能够保证完全可逆地恢复原始图像。

High Payload Reversible Data Hiding for Encrypted Image with Homomorphic Encryption

This paper proposes a novel reversible data hiding scheme for encrypted images with high payload based on homomorphic encryption. In this algorithm, each pixel of the original image is firstly divided into five parts, which are to be encrypted by applying the homomorphic application based on the Paillier algorithm. Then a serial of operations are carried out in the encrypted domain so as to embed the additional data into the encrypted image. Finally, the embedded additional data can be perfectly extracted, and the host image can be recovered without error when the marked image is decrypted directly. Security analysis, extensive experiment results and comparisons illustrate that it has high security, and the original image recovery is free of any error. Meanwhile, the embedding capacity of this algorithm is enhanced when compared with other literatures.

基于自适应中值预测和霍夫曼编码的密文域可逆信息隐藏算法

为提高密文域可逆信息隐藏的嵌入容量,提出了一种基于自适应中值预测(adaptive median edge detection,AMED)和霍夫曼编码的密文域可逆信息隐藏算法。所提算法首先对载体图像进行块级扩展。然后,预测阶段引入自适应参数提出中值预测(median edge detection,MED)优化方案,参数会随着图像纹理特征而变化,进而提高像素预测准确度。最后,对像素高有效位信息(most significant bit,MSB)进行标记,采用霍夫曼编码对标记结果进行压缩,从而腾出空间进行秘密信息的嵌入。实验结果表明,本文算法可以在保证可逆恢复的基础上实现秘密信息的正确提取。同时,与现有算法相比,嵌入容量有所提高,平均嵌入率高达2.6320 bpp。

差分编码和块压缩的密文域可逆信息隐藏算法

随着数字信息与云存储技术的发展与成熟,密文域可逆信息隐藏算法正逐渐成为通信传输中数据隐私保护的研究热点。如何根据不同载体图像像素的分布特征,对局部区域内像素进行自适应识别编码,实现对载体更彻底的压缩以提升嵌入容量仍具有较大挑战。本文针对密文域可逆信息隐藏算法因载体图像冗余空间压缩不充分而导致嵌入容量较低的问题,提出一种基于差分编码和块压缩的密文域可逆信息隐藏算法。首先,根据自然图像局部区域内像素强相关性邻位作差,并基于块内最大差值对差值图像矩阵进行分类和自适应编码,同时设计分块置乱和块内像素扩散加密机制保证图像信息安全。然后,将自适应编码结果在对应的密文图像块上进行标记,压缩冗余空间。最后,通过位替换嵌入秘密信息。不同于其他算法,本算法对差分编码后的图像进行比特位重排列,从密文图像像素最低有效位开始,标记差值像素的符号位,其余各位面依次标记最小差值比特位。由于加密操作与自适应编码的可逆性,合法接收者可实现对原始明文图像的无损重构和秘密信息的无误提取。实验结果表明,与现有的几类算法相比,本文提出的算法具有更高的嵌入率和更好的安全性,在BOSSbase和BOWS-2两个数据集上的平均嵌入率达到3.027位/像素和2.937位/像素,在测试图像上平均嵌入率也均提高了0.57位/像素以上。

密文域可逆信息隐藏研究进展及技术难点分析

密文域可逆信息隐藏技术融合了加密技术和信息隐藏技术的双重优势,旨在实现公开信道上数据内容的隐私保护和信息的可逆嵌入功能,以应对云环境下日益复杂的用户应用需求。本文从密态数据管理、情报隐蔽通信、军事协同作战、技术融合创新4个应用场景分析密文域可逆信息隐藏的研究价值,根据信息嵌入与数据加密的操作关系对密文域可逆信息隐藏分类,对不同嵌入框架下的技术发展进行阐述和总结。同时,从嵌入性能提升、可分离性实现、图像的加密安全性、满足云环境下的应用需求4个方面对密文域可逆信息隐藏的技术难点进行剖析,并对后续研究方向进行分析和展望。

基于顶点划分和坐标标准化的密文域3维网格模型可逆信息隐藏

密文可逆信息隐藏技术可以在加密载体中利用冗余空间额外嵌入信息,在传输过程中保障载体和信息的隐私安全,载体接收者还可以实现无损地提取信息和恢复载体.3维网格模型作为新型的数字媒体,其文件结构与传统的图像等数字媒体存在着不同,并且在该领域的研究相对较少.如何提升模型的嵌入容量是目前需要解决的问题.将图像领域多个高有效位预测算法直接迁移到3维模型中应用时,由于数据的存储格式与图像媒体不同,使得算法的预测性能受到了限制.因此,提出了将顶点坐标值标准化处理,消除符号位带来的影响,提升了预测算法的性能.为了进一步减少无用的辅助信息,嵌入集顶点的筛选被加入实验中,成功地为有效载荷腾出空间.实验表明,提出的方法与现有方法相比,在保证无损和可分离地恢复模型与所嵌入的信息的同时,获得了最高的嵌入容量.

基于像素预测和秘密图像共享的可逆信息隐藏

为增强图像加密的安全性以及增加加密图像的信息隐藏容量,提出一种基于像素预测和秘密图像共享的可逆信息隐藏算法。首先,利用共享矩阵逐行处理图像并分存为四个共享图像;其次,利用二维混沌映射生成随机密钥加密共享图像;再次,利用中值边缘检测器(MED)预测共享图像中可嵌入位置的像素值,预测值与原像素从高位开始比对相同的位数,根据规则记录标签值,提取参考像素的高三位与认证信息存入可嵌入位;最后,将标签值存入参考像素高位,剩余的可嵌入位为所提算法的嵌入容量。实验结果表明,所提算法不仅能够为信息隐藏提供大容量的嵌入空间,而且能够实现可逆数据隐藏并根据(k,n)阈值策略实现加密图像的无损复原。

用于实现医学图像对比度增强的密文域可逆信息隐藏算法

针对现有密文域医学图像可逆信息隐藏算法存在解密图像视觉质量较低的问题,提出了一种基于差值直方图平移的密文域可逆信息隐藏算法。首先,发送方采用具有同态密文比较性质的加密算法对原始医学图像进行加密,从而保证医学图像的隐私内容不被泄露。然后,嵌入方利用同态性质对接收到的密文图像计算差值直方图,并通过平移差值直方图在密文图像中嵌入信息。为了获得较大的嵌入率,嵌入方可对密文图像进行多轮次信息嵌入。最后,接收方根据拥有的密钥种类对接收到的含有嵌入信息的密文图像进行信息提取、图像解密和图像恢复。实验结果表明,本文算法提升了解密医学图像的视觉质量,同时具有较高的嵌入率和安全性。

High-payload completely reversible data hiding in encrypted images by an interpolation technique

We present a new high-payload joint reversible data-hiding scheme for encrypted images. Instead of embedding data in the encrypted image directly, the content owner first uses an interpolation technique to estimate whether the location can be used for embedding and generates a location map before encryption. Next, the data hider embeds the additional data through flipping the most significant bits （MSBs） of the encrypted image according to the location map. At the receiver side, before extracting the additional data and reconstructing the image, the receiver decrypts the image first. Experimental results demonstrate that the proposed method can achieve real reversibility, which means data extraction and image recovery are free of error. Moreover, our scheme can embed more payloads than most existing reversible data hiding schemes in encrypted images.

A Reversible Data Hiding with Quality Controllable Scheme for Encrypted-Compressed Image

In order to achieve image encryption and data embedding simultaneously, a reversible data hiding(RDH) algorithm for encrypted-compressed image in wavelet domain is proposed. This scheme employs the quality controllable parameter. Moreover it has larger embedding capacity and smaller quality control parameters than other methods in literatures. Meanwhile, the cross chaotic map is employed to generate chaotic sequences, and the total keys of the algorithm is far large. Experimental results and comparisons show that the proposed scheme has large capacity, high security, and strong resistance to brute-force.

基于最近邻像素预测的加密域可逆信息隐藏

针对现有的可逆信息隐藏算法在图像加密域应用场景下嵌入容量不高的问题,提出了一种基于最近邻像素预测的加密域可逆信息隐藏算法。充分利用自然图像的空间相关性,设计最近邻像素预测方法,计算预测误差,并进一步探究预测误差块分类规律,设计块内移位以及块重排方式,保留了块内相邻像素相关性。然后,通过动态标记块内误差区间以最大限度地空出冗余空间,达到提高嵌入容量的目的。选用5幅标准灰度图像及数据集BOSSbase、BOWS-2和UCID中的万余张灰度图像进行对比实验。结果表明:本文算法在安全性、可逆性和可分离性都满足的前提下,嵌入容量相比同类算法平均提升0.35~0.9 bpp(比特数)。

基于图像块高位平面无损压缩的可逆信息隐藏方法

提出了一种基于图像块高位平面无损压缩的密文域可逆信息隐藏算法.图像所有者对原始图像进行分块,通过块级别的加密算法进行加密,保留块内像素的相关性;信息隐藏者使用哈夫曼编码技术对图像块的高位冗余平面进行编码压缩,制造可用空间.接收者根据拥有的密钥实现数据提取、图像解密和图像无损恢复的分离操作.在不同纹理的图像测试中,本文算法的平均嵌入率达到了1.78 bit/像素,直接解密后标记图像的平均峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)为47.50 dB.测试结果表明:与同类算法相比,本文算法能够显著提高信息的嵌入率.