19-10-2016, 03:23 PM
HIGH CAPACITY REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BY PATCH-LEVEL SPARSE REPRESENTATION
1459915815-ITIMP13.docx (Size: 27.84 KB / Downloads: 5)
ABSTRACT
Reversible data hiding in encrypted images has attracted considerable attention from the communities of privacy security and protection. The success of the previous methods in this area has shown that a superior performance can be achieved by exploiting the redundancy within the image. Specifically, because the pixels in the local structures (like patches or regions) have a strong similarity, they can be heavily compressed, thus resulting in a large hiding room. In this paper, to better explore the correlation between neighbor pixels, we propose to consider the patch-level sparse representation when hiding the secret data. The widely used sparse coding technique has demonstrated that a patch can be linearly represented by some atoms in an over-complete dictionary. As the sparse coding is an approximation solution, the leading residual errors are encoded and self-embedded within the cover image.
EXISTING SYSTEM
EXISTING CONCEPT:
• Dual-images reversible data hiding methods can embed massive condential messages and generate an excellent stego image. However, each condential message and the cover image cannot be recovered if any stego image is missed. In order to overcome this drawback, we proposed a (k, n)-image reversible hiding method that can restore the cover image and k condential images from n stego images. Additionally, (n - k) cheating images can be embedded into stego images, which can deceive hackers or increase their analysis cost.
EXISTING TECHNIQUE :
• IMAGE REVERSIBLE DATA HIDING METHOD
TECHNIQUE DEFINITION:
• Since dual-images reversible hiding method modifies pixels by only adding or subtracting one, the stego image has an excellent quality. Furthermore, massive confidential messages can be embedded into two images by using this method.
PROPOSED SYSTEM
PROPOSED CONCEPT:
• To better explore the correlation between neighbor pixels, we propose to consider the patch-level sparse representation when hiding the secret data.
• The widely used sparse coding technique has demonstrated that a patch can be linearly represented by some atoms in an over-complete dictionary.
• As the sparse coding is an approximation solution, the leading residual errors are encoded and self-embedded within the cover image.
PROPOSED ALGORITHM:
• SPARSE REPRESENTATION
ALGORITHM DEFINITION:
• For reserving room to hide data, we train the dictionary based on K-means singular value decomposition (K-SVD) algorithm, which is widely used for designing over-complete dictionaries that lead to sparse signal representation.
DRAWBACKS:
• As the entropy of encrypted images is maximized, it is difficult to losslessly vacate room after encryption (VRAE) using the existing methods.
• In the existing systems, hiding Capacity of the secret data bits is low.
• If the receiver has only the data-hiding key, he can extract the additional data.
• It is slower process and complex nature andso the accuracy is low.
ADVANTAGES:
• It addresses both spatial and temporal domains, which leads to detecting various malicious changes in spatial and time domains.
• It is faster and lower complexity compared to existing algorithms, making it practical and suitable for real-time applications
• Hiding Capacity of the secret data bits is high.
• Hiding capacity was based on the pixel number corresponding to the two highest peaks of the image histogram
APPLICATIONS:
• Mathematical algorithms and keys to get back the original data from cipher code, scientific communities have seen strong interest in image transmission.
• Confidential transmission, video surveillance, military and medical applications. For example, the necessity of fast and secure diagnosis is vital in the medical world.
• Applications such as medical image system, law enforcement, remote sensing, military imaging
HARDWARE REQUIREMENTS:
• Processor : Pentium Dual Core 2.00GHZ
• Hard Disk : 40 GB
• RAM : 2GB (minimum)
• Keyboard : 110 keys enhanced
SOFTWARE REQUIREMENTS:
• MATLAB 7.14 Version R2012
FUTURE ENHANCEMENT:
In future research, the effects of compression and mobile transmission our scheme has the satisfactory performances for hiding capacity, compression ratio, and decompression quality through color space and different dimensions.