Comparative analysis of reversible data hiding schemes

IET Image ProcessingVolume 14, Issue 10 p. 2064-2073 Review ArticleFree Access Comparative analysis of reversible data hiding schemes Asha Jose, Corresponding Author Asha Jose ashajose07@gmail.com Karpagam Academy of Higher Education, Karpagam University, Coimbatore, Tamil Nadu, IndiaSearch for more papers by this authorKamalraj Subramaniam, Kamalraj Subramaniam Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, IndiaSearch for more papers by this author Asha Jose, Corresponding Author Asha Jose ashajose07@gmail.com Karpagam Academy of Higher Education, Karpagam University, Coimbatore, Tamil Nadu, IndiaSearch for more papers by this authorKamalraj Subramaniam, Kamalraj Subramaniam Department of Electronics and Communication, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, IndiaSearch for more papers by this author First published: 01 July 2020 https://doi.org/10.1049/iet-ipr.2019.1066Citations: 2AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract The data hiding method embeds the data into covers such as video, audio, and images, which are used for integrity authentication, media protection, communication covert, copyright protection etc. In this work, several reversible data hiding (RDH) algorithms are analysed. Here, RDH algorithms are classified into six categories. They are histogram shifting centred RDH, code division multiplexing-based RDH, compression-based RDH, contrast enhancement with RDH, and expansion-based RDH, and RDH for encrypted images. In RDH,no information is lost when the message is recovered as the message is embedded into the host image. The embedded message is extracted using an extraction procedure. This study critically reviews the technological advancement for the past decade on the aspects of classification, pre-processing, feature extraction, and analysis technique. The current trend in RDH is to insert confidential data into a video by using RDH in the encoded area that has a wide selection of applications on the confidential communication inside the area of cybersecurity. 1 Introduction Data security is necessary for transmitting the data from one end to another end around the network. The confidential data is secreted with a cover image in steganography. In this steganography, Steganos indicates 'covered' and graphic indicates 'writing' [1]. The data hiding technique is divided into three phases viz. attacking, extracting, and embedding. Secret data is embedded within a cover image, using the embedding algorithm and the secret key. Also, an extraction process is performed with the secret key and extraction algorithm. In the case of transferring these files, the secret messages may be corrupted by some noise or attacked with someone and this stage is represented as attacking stage [2]. The major solution to data security is data hiding, which enhances security while transferring the data. In data hiding, secret data gets hidden within digital media by creating non-perceptible small modifications to cover media. Normally, the process of data hiding is categorised into reversible and irreversible types. In the case of reversible type, the secret message which is extracted from the hidden image is to be zero loss that is, it yields without distortion data. In the case of irreversible, the outcome is distorted one, i.e. the lossy outcome is produced at the output. Some of the techniques related to this data hiding process are investigated for predictive ability analysis of a new prediction scheme [3] also, adjusting a part of encrypted data using the method named reversible data hiding (RDH) [4, 5]. There are a few applications where we utilise the data-hiding concept [6] Private communication Image authentication Fingerprinting Fraud detection Copy control Commonly, embedding capacity is low for RDH while comparing it with irreversible type. Moreover, this reversible type represents high-image-quality while comparing to the irreversible data hiding methodology [7]. 1.1 Properties of data-hiding strategies The most important properties that arise in the data hiding strategies are given below: Security: This is available to withstand the targeted attacks, which cannot get the inserted data without the knowledge of secret-key and embedding algorithms. Robustness: This is the property in which the hidden information can be retrieved using any of the image processing strategies such as linear, a non-linear filter, swapping frames, adding noise, and resampling adjusting scale. Invisibility: This is the property related to human perceptual transparency. Capacity: It is the difficulty in detecting the existence of any hidden message in the carrier image [8]. The way of hiding data in a file such as audio, video, and image is called RDH. This technique makes the receiver could recover both the data and cover file without loss. Barton proposed an RDH algorithm of US patent in 1997. They are applicable in medical image processing, stereo image coding, engineering graphics, image authentication, and vector map retrieval in computer-aided design etc. [9]. This RDH method is used as a part of many fields such as law forensics, military, and medical applications [10, 11]. Fig. 1 displays the basic structure of the RDH method. Embedding is an initial stage at which a private message is given to the cover image for hiding personal information. This message can play as an input to the embedding algorithm. After covering the picture, the output will be in the form of the covered image. Then this result is given as an input to the extracting algorithm which separates the private message from the cover image. The secretive data and cover image must be in the original form and hence named as RDH. Fig. 1Open in figure viewerPowerPoint Basic structure of RDH Contribution related to this survey is given below: In such cases, embedding distortion is not tolerable so we cannot use it for further important applications such as military, medical etc. So slight changes lead to improper treatment in medical applications also in remote sensing this may differently predict the cover area. So the embedding process should be an efficient one to make the process as secure. This literature survey about various RDH techniques and extraction processes are useful for further applications. This work aims to provide a survey about new classification techniques related to RDH and provide their discussions. The RDH is classified into six categories. The outline of the work is given below. Section 2 discusses the recent survey papers based on RDH. Section 3 discusses RDH and their classifications. In Section 4, we have to discuss the performance metric of such RDH and this survey concludes in Section 5 with the future direction. 2 Related work Zhang et al. [12] introduced reversible, lossless and also combined data hiding approaches for ciphertext images. These images were encrypted by the encryption scheme named public-key cryptosystems, using dual properties namely homomorphic and probabilistic. The pixel division or else reorganisation was avoided and encryption or decryption was performed on the cover pixels directly to minimise the computational complexity and encrypted data. Ma et al. [13] proposed a new method by reserving room prior to the encryption process using the RDH traditional algorithm. Also, this method seems easier for data hider to embed data reversibly in encrypted image. Image recovery and data extraction were free from error. Experimental outcomes showed that the proposed method can embed large payloads and attained outstanding performance without losing secrecy. Hu et al. [14] proposed an embedding scheme that helped the authors to create a payload-dependent overflow location map. This map aimed to minimise unnecessary image alteration because this map has the capacity of good compressibility. The algorithm of this work represents a larger embedding capacity under similar image quality. This method performs better in dissimilar types of images compared with other algorithms whereas the existing approaches face issues in acquiring high image quality and better embedding capacity. Chang and Wu [15] presented the data hiding reversible approach for digitally compressed images, based on side match vector quantisation (SMVQ). A two-step process was performed by the receiver with the presented concept. In the first step, the secret data was extracted and in the next step, the original SMVQ compression codes were reconstructed. Experimental outcomes showed that for vector quantisation and SMVQ-based compressed images, the proposed approach is better than the other information hiding schemes. The performance was measured and analysed in terms of visual quality, compression rate, and secret data size. Kim et al. [16] proposed RDH to modify the subsampled images. It exploits the high spatial correlation inherent in neighbouring pixels to achieve high capacity and imperceptible embedding. The proposed method was tested on different test images together with 16-bit images and compared with other existing algorithms. Experimental outcomes indicate that the proposed approach offers higher embedding capacity while keeping distortions at a low level. Hong et al. [17] introduced a data hiding reversible scheme based on the concept of the modification of prediction error (MPE). The histogram of prediction errors (PEs) was modified using the proposed method to formulate vacant positions of data embedding. The peak signal-to-noise ratio (PSNR) of stego-image produced using MPE was obtained 48 dB. The embedding capacity is higher than the methods with similar PSNR. Moreover, MPE can also be applied to images with 5665a flat histogram whereas fewer data bits needless error modification. Zhenfei et al. [18] proposed an RDH approach based on histogram modification mainly for natural images. The main aim was to modify the histogram instead of the host image's histogram based on neighbour pixel differences. Also, the multilevel modification was employed on the data embedding stage. Xiong et al. [19] presented an adaptive RDH approach based on difference expansion (DE) prediction. As every cover pixel resembles the surrounding pixel, the difference among predictive pixels and cover pixels was small. Hence, the proposed approach gains from embedding capacity by considering the full benefit of large quantities where secret data can be embedded. Yu and Wang [20] proposed an RDH approach to restoring the original image after the process of data extraction. Here, space-filling curves were used to reorder the cover image into a sequence. Also, a pixel difference was generated among the pixel and neighbouring pixels. The correlation of adjacent pixels was caused due to inherent image structures. The embedding capacity was improved by space-filling curve scanning with low distortion. Lin et al. [21] presented a high payload reversible scheme based on the compression domain named absolute moment block truncation coding (AMBTC). To verify whether the block is embeddable or not, redundancy of AMBTC compressed images was discovered. Then a set of four disjoint sets was created based on the combinations such as mean and standard deviation for embeddable blocks. Comparison performance indicates the effective nature of the proposed approach while comparing it with other methods. Xiong et al. [22] invented an RDH method that is based on histogram shifting (HS), integer wavelet transform and orthogonal decomposition. This method used high-frequency sub-bands with integer wavelet high-frequency coefficients. Also, it used independent orthogonal coefficients to simplify the data hiding process in the encrypted domain to maintain the reversibility. Parah et al. [23] introduced an RDH and high capacity scheme. Here the technique related to pixel to block conversion had been used as a computationally efficient and effective method to interpolate the generation of the cover image for ensuring medical image reversibility. A fragile block checksum and watermark was hidden in the cover image for simplifying tamper localisation and tamper detection. The electronic patient record (EPR), checksum data, and watermark data had been hidden using intermediate significant bit substitution for avoiding generally used replacement attack/least significant bits (LSBs) removal. For the encryption purpose in clinical data/EPR, non-linear dynamics of chaos is introduced to improve the security. Chen et al. [24] proposed a directional enclosed predictor and this was used to identify the locations in which local complexity (LC) is not relative to PE. Also, a directionally-enclosed prediction and expansion (DEPE) scheme was used for efficient RDH. With DEPE, data embedding was limited to pixels in which LC associates with PE through a proportional relationship. 3 Taxonomy of RDH and their classification The classification of RDH is presented in Fig. 2. Fig. 2Open in figure viewerPowerPoint Classification of RDH RDH techniques have been classified into six categories. They are expansion-based RDH, HS-based RDH, code division multiplexing (CDM)-based RDH, compression-based RDH, RDH with contrast enhancement and RDH in the encrypted domain. Each type is explained and reviewed in the document. 3.1 Expansion-based RDH The fundamental technique of RDH is named DE and it is broadly developed and investigated based on the following techniques such as PE expansion (PEE), adaptive embedding (AE) and then integer-to-integer transformation (ITIT). The ITIT approach basically splits the cover image into blocks then data is embedded in the block-wise approach. These techniques benefit from the impact of location map minimisation mainly for embedding performance. Moreover, ITIT-based approaches make use of less effective prediction to predict each of the pixels within the block. In [25], Coltuc and Chassery introduced an approach named reversible contrast mapping, which follows the same concept of ITIT for every integer pair. Comparing with DE, the main feature of this new approach is that there is no need for extra lossless data compression and it is effective in terms of computational complexity. Weng et al. [26] introduced improved DE using dual ITIT of each pixel pair based on pairwise difference adjustment and the sum of pixel pairs by considering the difference value magnitude. Then, Wang et al. [27] formulated the DE approach using a new ITIT, which showed the embedding rule based on integer pair transformation. Also, Qiu et al. [28] recently developed the extended version using AE. Thodi and Rodríguez [29] proposed the PEE method and this was widely used by many RDH works. A pixel predictor was utilised by PEE rather than considering the difference operator in DE. One of the significant strategies of RDH is named AE, which is very useful for performance enhancement. Kamstra and Heijmans [30] proposed the first adaptive RDH scheme based on sorting and DE. Tian's DE approach [31] was enhanced by pixel pairs sorting based on a local variance in advance of the data embedding approach. The difference value in DE gets altered as the value of the integer pair of pixels is invariant. Thus, encoder and decoder are used by the embedding invariants to calculate local variance. 3.2 HS-based RDH HS [32] is the best method for RDH. In this RDH method, a histogram is created initially, after that, by adjusting the generated histogram, the reversible data embedding is executed. In [33], Xuan et al. suggested a HS technique that yields better execution. The embedding distortion had been minimised by deciding the suitable histogram pair and utilising it for a given payload. There are two sequential integers that are characterised by either or and with the histogram counting g and integer wavelet transform coefficients. A normal structure to design an HS based on the characteristics of RDH is introduced in [34], however various traditional schemes are included in this construction as a special condition. The following process flow is accomplished by this general framework, initially, the cover image is divided into various non-overlapping blocks, where each block is provided with n pixels. The frequency of several divided blocks is then counted to generate the n-dimensional histogram. Finally, the resultant n-dimensional histogram is modified to implement the data embedding. The rhombus prediction was introduced by Sachnev et al. [35]. During rhombus prediction, an integer value from four nearest neighbours was averaged to predict the centre pixel. The reversibility process of this technique is guaranteed by applying the double-layered embedding mechanism. In this mechanism, initially, the cover image is split into two different sets they are 'cross' and 'dot'. After accomplishing the separation process, both cross and dot sets are embedded within the half portion of the secret message. Along with that, a sorting strategy is included to improve performance. 3.3 CDM-based RDH A unique RDH based on CDM is proposed in [36]. In this method, diverse orthogonal spreading sequences (OSS) were applied to represent the secret data and then embed this secret data within the cover image. Here, the Walsh Hadamard matrix was introduced for OSS generation, through this technique, the data is embedded in an overlapping manner without causing any interference to each other. Owing to this, an enlarged embedding capacity was achieved by this technique. The best performance was achieved by this CDM-based RDH with medium-to-large embedding capacity than other existing schemes. 3.4 Compression-based RDH Joint Photographic Experts Group (JPEG) standard introduced the balance among the compression rate and visual quality of the compressed image. The JPEG format is extensively applied by various photo capturing strategies and digital cameras. Hence, JPEG images were selected in RDH as the best candidates for cover objects. JPEG compression eliminates few high-frequency components from the image to minimise the image size, however, this RDH process aims to improve the quality of real image during this compression process. Some essential facts about encoders were exploited by Mobasseri et al. [37]. Most particularly, about the occupancy of JPEG code space by available encoders. The variable-length code (VLC) is mapped with the unused VLC to perform data embedding. Owing to this, the file size may get reduced or maintained at the same level without considering the payload. Qian and Zhang [38] mapped the unused VLC with the usable codes, according to the statistical results of VLC usage. Based on secret bits, the mapped codes replace the few code portions from the bit-stream while performing data hiding. After that, the full merits of unused VLC were considered by Hu et al. [39]. In this, the Huffman codes are mapped with a particular mapping strategy to improve the embedding capacity and also to achieve maximum data capacity. A novel integer-vector conversion algorithm was introduced by Wu and Deng [40] in Huffman tables of JPEG. Owing to this, alterations in bitstreams occur simultaneously along with the embedded message. 3.5 RDH with an enhancement of contrast RDH is normally developed for authentication, which is most particularly applied in distortion sensitive applications such as military, satellite, and medicine. Preserving the perceptual quality of the host image is identified as the major role of this scheme. Moreover, the quality of the image having hidden data needs to be preserved using a conventional RDH approach, meanwhile, the PSNR should be maintained in a high range. Four different RDH techniques with contrast enhancement were reviewed here. They are histogram bin expansion [41], contrast enhancement for medical images [42], controlled contrast enhancement [43], and automatic contrast enhancement method [44]. Histogram equalisation is applied to achieve the contrast enhancement in the image [45]. The contrast enhancement and data hiding were made through the pixel value of the modified histogram [41]. The number of the pixel value is calculated by the host image with a range of 0–255. The artificial distortions were included in the images with a strong background and it is explained in [41]. In [42], an enhanced form of RDH is provided, which is widely applied to eliminate the impacts in the medical-based images. Otsu's method [46] is initially applied in this method for background separation which separates the image into two as a region of interest (ROI) and background. The pixel value having the range higher than the pre-defined percentage is selected from the segmented background as the fundamental one. A similar process followed in [44] is applied in this method during extraction for hidden data extraction. The entire expanded histogram bins are presented within the contrast-enhanced image, therefore it is necessary to perform the extraction process in this method. At the same time, the location map hidden within the contrast-enhanced image is extracted to blindly recover the original image. The visual distortion obtained in [44] is eliminated by a technique in [47], to enhance the data hiding capacity. Upper bound characteristics of relative contrast error (RCE) are well defined in [48] to control the degree of contrast enhancement. This control process is carried out to prohibit the over-enhancement. In this, the image having an RCE value higher than 0.5 is identified as a high-contrast image, however, the upper bound for this RCE is set as 0.55 for preventing the visual quality. The Haar integer wavelet transform is introduced for data hiding which enhances the hiding capacity. Through this, high-image-quality is achieved for those images having high hiding capacity. 3.6 RDH in the encrypted domain (RDH-ED) The most popular and effective technique for privacy protection is encryption. Recently, the encryption is carried out in signal processing, furthermore, it also received a huge demand in cloud computing and also in different privacy-preserving applications. However, the encryption and data hiding combination had already received huge attention in these applications. An intra-prediction mode is introduced in [49], in this, the encryption process is carried out over the signs and motion vector difference of discrete cosine transform (DCT) coefficients, meanwhile, embed the watermark process within the amplitudes of DCT coefficients. In [50], the watermark and encryption are applied on the cover data of lower and upper bit-planes of the transform domain. The cover data is found in the lower and upper bit planes of the transform domain, whereas this cover data is watermarked and encrypted in [50]. Sometimes, in this combined process, the partial information from the cover data may get lost due to the presence of partial encryption. Moreover, in this method, the process of separating the embedded data and original cover from the watermarked version is not taken into consideration. Furthermore, the data embedding is also found irreversible. Inserting private data into video using RDH techniques is a trending topic. Video data hiding is a technique used to hide the data in a video in a secure way by using the concepts in the human visual system. In general, the video consists of a still image captured during different times. As these images run continuously and motion picture is viewed. Therefore, these still images are also called frames. Video steganography in the research field is considered to be the extended work of image steganography [51–55]. The general framework of RDH-ED is having three different parties in the overall workflow: After encryption of vacating room (VRAE) Before encryption of vacating room (VRBE) 3.6.1 Before encryption of vacating room (VRBE) In the plaintext domain, the VRBE framework generates the embedding room, i.e. before encryption, the embedding room gets vacated [56–59]. Thus, it is essential to perform extra pre-processing before encryption by the data owner. In [60], the conventional RDH technique is applied to embed the LSBs of particular pixels within the other pixel so the embedding room may get created within the particular digital image. Then, the owner encrypts the pre-processed image for generating an encrypted image. Therefore the positions in the encrypted image that are vacated by the LSBs are utilised by data-hider, due to this, a large payload of about 0.5 bpp is achieved by this technique. In [61], an approach based on prediction is introduced. Before performing the encryption process in the method, a few pixels are identified by the remaining pixels and a high gain in PE is achieved. Then, these predicted errors are encrypted with a special encryption algorithm and a benchmark algorithm based on encryption [e.g. advanced encryption standard (AES)] is applied for remaining pixels. The data embedding process is not directly carried out in this encrypted images, rather an additional data is embedded within it by predicted errors encrypted HS. Finally, the patch-level sparse representation is considered for achieving further improvement [62]. The combination of traditional plaintext RDH technique and Paillier homomorphic encryption is normally referred to as DE, which is introduced in [60] to vacate the embedding room. For this, the pre-processing is essential. Otherwise, the image owner needs to pre-process the image before performing the encryption process. This pre-processing is required to develop a preprocessed image along with a modified DE technique. Then the Paillier homomorphic-based encryption is applied to encrypt the preprocessed image and then transfer this image to the data-hider. This data hider will embed the single bit within the several pair of neighbouring encrypted pixels, then a marked encrypted image having additional bits is developed. Owing to the presence of homomorphic property in Paillier encryption, the receiver performs a comparison among the decrypted pixels for attaining the cover image recovery and embedded bits. 3.6.2 After encryption of vacating room (VRAE) In VRAE, the encryption is directly carried out by the owner in the original signal. After that, few bits in encrypted images are modified by data-hider for embedding the additional data. According to the domain, the VRAE is further classified into three basic categories, through which additional data extraction can take place. The three categories are data extraction in the plaintext domain, in the cipher domain, and in both domains. 3.6.3 Plaintext domain data extraction The initial method for image encryption is introduced by Zhang and co-workers [60], in this technique, the encrypted image is divided into blocks and the one bit is embedded within several blocks by flipping the three LSBs of the partial pixels in the block. The marked encrypted image is decrypted by the receiver to a qualified image. The three LSBs are flipped by the receiver to generate a block. Also, a function is utilised to determine texture from the image blocks. The spatial correlation is experienced in natural images, due to this, the smoothness of the original block is found much better than the interfered block. After that, extract the embedded bits, meanwhile, the original image is also recovered. In this method, the embedding rate highly depends on block size. The selection of incorrect block size causes errors during image recovery and data extraction process. In [61], the spatial correlation among the neighbouring blocks is exploited to improve the system performance and then the algorithm named side-match is applied to attain the maximum payload with minimum error rates. During image recovery, the flipping ratio [62] and unbalanced bit flipping [63] are employed to further improve the performance. 3.6.4 Data extraction in The cipher domain AES was exposed by Zhang et al. [64] in which the original image was initially encrypted by the owner. There are n pixels in the image so, the data-hider inserts a single bit in every single block which means the rate of embedding is 1/n bpp. Investigation of local standard deviation is performed to realise the data extraction and image recovery in the receiver side's encrypted image. The embedding rate is better in this method even though it contains two major problems on the receiver side. The codes that are selected from the stream-ciphered image are encoded within syndrome bits in [60], by low-density parity check codes so that the spare room that required accommodating the extra data is generated. 3.6.5 Data extraction in plaintext and cipher domains Additional bits are included within the marked encrypted media, so the legal receivers having the key of data hiding alone can directly extract data embedded from the cipher domain. Then, the content used with the encryption key performs the decryption process. Also, those who receive appropriate media along with the data-hiding key, successfully attain the extra bits. With these bits, the receiver can successfully recover the real image. According to pseudorandom sequence modulation, the primary solution is provided in [65]. Here, extra data is included with the encrypted image to replace the particular data portions in LSB planes and the remai