An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images using matlab


Reversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryption key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. However, none of the existing methods allows us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity.

The existing system uses the Discrete Wavelet Transform (DWT) only for encryption and decryption as well.

1. More complex.
2. Less accuracy.
3. Low PSNR and MSE.

Image encryption algorithms and hiding algorithms should be designed to enhance the effectiveness of transmission and keep safety from attacks by the intruders. The proposed method can achieve the highest level of data integrity, confidentiality and security. We trying to verify the confidentiality of gray scale image that makes uses of pixel shuffling and DWT stream cipher for cryptography and Hash-LSB for steganography. The main function of the pixel shuffling is that it involves no modification in the bit values and no expansion of pixels in the end of the encryption and the decryption procedure. The pixel values are redesigned and combined moving from their particular positions and then the values are swapped to give the cipher image which becomes recognizable. 

1. The proposed method can achieve the highest level of data integrity, confidentiality and security.
2. More accurate.
3. High PSNR and MSE.



Processor Type : Pentium -IV
Speed : 2.4 GHZ
Ram : 128 MB RAM
Hard disk                                : 20 GB HD

Operating System           : Windows 7 
Software Programming Package           : Matlab R2014a


A discrete wavelet transform (DWT) is any wavelet transform for which the wavelets are discretely sampled.
As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution
It captures both frequency and location information (location in time).
Wavelets are often used to denoise two dimensional signals, such as images
Wavelets, by contrast, have both frequency and location. As before, the first completes zero cycles, and the second completes one cycle. However, the third and forth both have the same frequency, twice that of the first. Rather than differing in frequency, they differ in location — the third is nonzero over the first two elements, and the forth is nonzero over the second two elements.


In digital steganography, sensitive messages may be concealed by manipulating and storing information in the least significant bits of an image or a sound file.
In the context of an image, if a user were to manipulate the last two bits of a color in a pixel, the value of the color would change at most +/- 3 value places, which is likely to be indistinguishable by the human eye. 
The user may later recover this information by extracting the least significant bits of the manipulated pixels to recover the original message.


In computing, the most significant bit (MSB, also called the high-order bit) is the bit position in a binary number having the greatest value. 
The MSB is sometimes referred to as the high-order bit or left-most bit due to the convention in positional notation of writing more significant digits further to the left.
The MSB can also correspond to the sign bit of a signed binary number in one s or two s complement notation, "1" meaning negative and "0" meaning positive.


By encoding and processing the image information in quantum-mechanical systems, a framework of quantum image processing is presented, where a pure quantum state encodes the image information.
To encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Given a image , where represents the pixel value at position with and , a vector with elements can be formed by letting the first elements of  be the first column of , the next elements the second column, etc.
A large class of image operations is linear, e.g., unitary transformations, convolutions, and linear filtering. In the quantum computing, the linear transformation can be represented as with the input image state and the output image state . 
A unitary transformation can be implemented as a unitary evolution. Some basic and commonly used image transforms (e.g., the Fourier, Hadamard, and Haar wavelet transforms) can be expressed in the form , with the resulting image  and a row (column) transform matrix . 
The corresponding unitary operator can then be written as . Several commonly used two-dimensional image transforms, such as the Haar wavelet, Fourier, and Hadamard transforms, are experimentally demonstrated on a quantum computer, with exponential speedup over their classical counterparts.
In addition, a novel highly efficient quantum algorithm is proposed and experimentally implemented for detecting the boundary between different regions of a picture: It requires only one single-qubit gate in the processing stage, independent of the size of the picture

A digital watermark is a kind of marker covertly embedded in a noise-tolerant signal such as an audio, video or image data.
It is typically used to identify ownership of the copyright of such signal. "Watermarking" is the process of hiding digital information in a carrier signal
The hidden information should,but does not need to, contain a relation to the carrier signal. Digital watermarks may be used to verify the authenticity or integrity of the carrier signal or to show the identity of its owners. 
It is prominently used for tracing copyright infringements and for authentication.


W. Lu, A. Swaminathan, A. L. Varna, and M. Wu, “Enabling search over encrypted multimedia databases,” in IS&T/SPIE Electronic Imaging. International Society for Optics and Photonics, 2009, pp. 725 418– 725 418

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