Encrypted image-based reversible data hiding (EIRDH) is a relatively popular and commonly used technique for hiding information. In encrypted image-based reversible data hiding, there are typically three common entities namely; the image provider, which is commonly referred to as the “context owner”, the data hider and the data receiver. All the EIRDH have to hold a shared key in the data hiding process. Essentially, the image provider sends the entity for hiding data the encrypted image through encrypting a select cover-image.
The data hider then subsequently creates the message-embedded encrypted image to the data receiver by embedding the already encrypted image to the hidden message being sent. In the data hiding and transmission process, the entity that receives is capable of simultaneously recovering the originally sent cover-image and thus extracts the initially intended and correct hidden message.
Nevertheless, the entity that hides data and the one that provides the image should be designated parties. This paper gives a proposition for the combined data hiding and reversible schemes majorly for cipher-text images which are commonly encrypted by the public-key cryptosystems that have both homomorphic and probabilistic properties.
During the reversible scheme process, a preprocessing is used in shrinking the histogram of the image before the image is encrypted, so as the modification that is done on the images that are encrypted for embedding data cannot cause oversaturation of the pixels in the domain of the plaintext. Despite the introduction of a very slight distortion, the data that is embedded can be easily extracted and then the original image be recovered from the image that has already been decrypted.
Currently, there is a lot of interest in the research of data hiding in images that are encrypted thus explaining the devotion in the funding towards these researches. This data encryption process protects assets such that the initial cover is improved in a lossless manner and afterwards the data that is embedded is deleted while at the same time protecting the privacy of the content of the image. With the increased use of the Internet, it is very crucial for the data sender to encrypt data before transmission so as to protect the data privacy. Reversible data hiding techniques give confirmation that the data receiver receives the hidden sent messages and extract the initially intended information without distorting the data. The reversible data-hiding technology has attracted massive attention since the recoverable media are considered more valuable when actually providing protection to the data privacy and security. Within the spatial domain, the pixels of the cover image explicitly perform the image conversion in order to hide the data that is being encrypted while in the domain of the distorted area, the cover image undergoes processing through a process of transformation for it to achieve the requisite coefficients of frequency. Subsequently, the coefficients of the frequency are enhanced in the actual data hiding. The compression domain on the other hand is where the hidden data is subjected to codes of data compression to hide the data.
The initial image division step involves the innovative separation of uncompressed image into two basic fragments of the data, i.e., fragment A and fragment B, and then afterwards monitored through the LSBs. Fragment A is reversibly embedded into fragment B. The embedding is done through the self-reversible insertion and the technique of hiding data reversibly. The LSBs of fragment A can then be used in the putting up of the extra data. The self embedded data can then be reorganized through the encoding of the images using stream ciphers. High Capacity Technique for embedding data in a lossless manner As mentioned earlier, the technology of information insertion over pictures has attracted massive enthusiasm. The information insertion process can utilize either the lossless or the lossy strategies of data encryption. The lossy strategy, however, has the probability of imposing a concealing limit to the data. This leads to the limitation of not being able to recoup the host picture with the requisite constancy. A number of applications need the careful recovering of the original host picture such as the process of understanding drug information can be easily implanted without necessarily influencing the picture that is to be restored( Laskar & Hemachandran, 2012). Needless to mention, the procedures for lossless hiding of data have the probability of being affected by the ill effects of the restrained limit as the original host picture has to be kept put and in place. In this research particularly, the lossless methodology of data concealing is proposed. The lossless strategy also entails the use of picture histograms as investigated in the recognition of the limit for installing the multiple and diversified sorts of pictures. The histogram properties of maxima and minima are both utilized in the insertion of the estimation of the limit. The proposed strategy usually give the limit of concealing that usually goes up to half of the original host picture size for the pictures that have wide and vast homochromatic districts.
The currently used distinction extension (DE) systems of installing perform a single layer of implantation into a distinction picture. The systems don’t swing to the next contrasting picture for another layer of data concealing thus leading to the insertion of the information. The hindrance that comes along with these procedures is the fact that the quality of pictures obtained in the process may have been debased during the process leading to the implantation of that begins on the grounds that the past layer has spent for every expandable contrast (Ni, Shi, Ansari & Wei, 2006).
The process of advanced watermarking which is commonly alluded to the covering up of information has been proposed as the most promising procedure for confirming data. Inferable from the stowing away data and information, some changeless information bending may happen and thus consequently leading to the initial cover medium that will most probably be not possible to be turned around even after the hidden data has been removed. This type of data hiding calculations can be alluded to as the lossy strategy of data hiding being lost. It can be further demonstrated that the major portion of the data hiding process calculations are reported to be of the lossy type (Zhang, 2011). The three most common and noteworthy classes of data hiding calculations are the spread-range water-stamping procedures, the round-off and/or truncation mistake is also probable to occur whilst implanting information, and the piece 8×8 DCT space (Zhang, 2011).
A speculation of the best understood slightest significant piece (LSB) change is identified as the best strategy for insertion of information, which presents the best working limit mutilation bend. The lossless recovery of the initial data is attained by the packing of the segments of the signs that are rather helpless in the implantation of the mutilation and the transmission of these relatively compacted portrayals as the pieces of the payloads that are installed. The forecast mistake extension-based reversible data hiding plans are made up of two stages. First, there’s the initial acute expectation mistake of histograms produced by the use of the pixel forecast methods. Secondly, there’s the unknown messages that are implanted into the expectation mistake but which are reversible at the same time. The blunders arise due to the growing and the moving of the histogram. In this module, the initialization of the given original image occurs. This implies that the original image from the sender is obtained by the use of the keyword ‘uigetfile’. The keyword contains the filename and the pathname. For the user to read the filename of the image the command, ‘imread’ is used. The read image is usually stored in a variable as a matrix. The size of the image is then estimated using the ‘size’ command. This type of information obtained for the image is crucial for estimating if the given text is within the size of the original input image.
Encryption of the image In this phase of data encryption, the results of the bits of the original image and the bits of the pseudo-random nature are both calculated.
The data embedding phase In this phase, most parameters of the image are embedded into a relatively minute number of the encrypted pixels, while at the same time the LSB of the rest of the encrypted pixels are compressed further so as to create more space for the accommodation of the additional data and the original data at the areas that were originally occupied by the parameters. Based on the key for concealing data, the data-concealing pseudo unselectively selects the N, encrypted pixels that are later used in carrying the parameters used for hiding the data (Yunpeg, Yu, Zhong & Sinnott, 2011).
The extraction of data and the subsequent recovery of the images In the data extraction and image recovery phase, there are three most prevalent cases. First, the receiver could be having only the data-hiding embedded data, the receiver could be in possession of the key for encrypting data only and finally the receiver could be having the both the data-hiding and the data encryption keys. With the encrypted image having the embedded data, if the data receiver has only the key for hiding the data, the receiver may have to first acquire the values of the parameters and also from the LSB of the LSB of the selected encrypted pixels. It is worthy to note that, due to the pseudo-random selection of pixels and the permutation, any attacker of the data who doesn’t have the key for hiding the data is not capable of obtaining the parametric values and the groups of the pixels, and thus cannot be in a position to effectively extract the data that is embedded. Additionally, even though the receiver who is in possession of the key for data-hiding can successfully extract the data that is embedded but cannot obtain any information about the original content of the image.
Computation of the PSNR Value In this module, the computation of the PSNR value occurs for the input image and the image that has already been decrypted. The Peak Signal-to-Noise Ratio is a term used to refer to the ration between the maximum possible power of the generated signal and the power of signal corrupting caused by the noise effect which in turn affects the fidelity of the signal representation. Due to most signals having a very widely varying and broad dynamic range, the PSNR is normally expressed in terms of the decibel scale logarithmically.
The stage of combined data hiding and embedding entails all the activities of transmitting the obtainable data in a way that the hidden data is protected. The sender of the data uses some algorithms for encoding and compressing the data and formerly inserting the bit stream into the encrypted image. The two processes of the combined data hiding and embedding process are the encryption and the compression process. encoded by the use of various different algorithms of encryption. The compression stage works efficiently in the shrinking of the size of the message. A table is generated in the exchange of the binary codes and the initial message that was intended to be sent. The table is shown to the message recipient at the end of the process for them to extract the unique secret message hidden in the encrypted encoded message.
As explained in this research paper, the process of reversible concealing of data in the encrypted image consists of the image encryption, the embedding of the data, and the image recovery stages. In the initial phase of data encryption, the sender of the data encrypts the original and raw uncompressed image using the key used for data encryption. In this phase, another key that is commonly referred to as the password is included for the decryption of the already encrypted image. The encrypted image usually has the additional data important for the data receiver for extraction of the hidden message in the encrypted image. When the receiver of the data has the keys for both data encryption and data decryption, they can then extract the additional data and use it for recovering the original content of the message without necessarily facing complexities by making use of the spatial correlation in the original image if the amount of the additional data isn’t too huge.