A fast and accurate steganography technique

Copyright, Digital Era

ABSTRACT

Communication technology has been advanced in the last several years, which enhances the requirements of secure data communication and information supervision. For this reason a large number of researchers possess exerted most of their time and efforts so that they can find suited ways for hiding information. The recommended technique shows an efficient safe-keeping security mechanism for the protection of digital medical images by simply improving the previous Discrete Wavelet Transform (DWT) techniques. The standard of the stego image as well as the recovered image showed acceptable visual quality. The effectiveness of the proposed scheme will be proven through the well-known of imperceptibility way of measuring Weighted Optimum Signal-to-Noise Ratio (WPSNR), Indicate Square Problem (MSE) and Normalized Mix Correlation Coefficient (NCCC). Experimental results had been compared to the previous techniques. Working environment for recommended system is MATLAB.

Keywords “DWT, steganography, dividing, sub-images.

LAUNCH

The copyright security of the digital multimedia like image, audio and video is very needed with the network and interaction development. Nowadays the impair environment became very popular and important for a large number of people being a platform intended for storing and retrieving enormous data. To be able to use it has to be secured. Steganography is a approach to maintain the confidentiality of the information being transmitted. Many methods just like cryptography, watermarking, fingerprinting and encryption and decryption tactics were advanced in order to make the knowledge secured during communication. Cryptography computation and key administration limit its employment and therefore the recovered photo has a low quality. Watermarking and fingerprinting are mostly concentrating on protecting the copyright laws property and also have different algorithms. In both equally watermarking and fingerprinting the truth that the top secret message hidden inside the jar may be noticeable. But in steganography the fact that there is a top secret message invisible inside a record itself will be a secret.

Most commonly used extendable for interaction is Digital Image due its higher frequency on the Net. The image come after hiding process is described as as stego image.

Steganography is definitely categorized into two types: Spatial domain and Transform domain name. In space domain methods the secret data is hidden in the strength of the px directly. The most popular widely used and simplest steganographic technique inside the data concealing is least-significant-bit (LSB) substitution. The process of embedding data in the transform domain name (frequency domain) is much more powerful than embedding techniques that operate in the time domain name. Today a lot of the strong steganographic systems operate in the change domain. Numerous applications will vary requirements of the steganography technique used. For instance , some applications may require complete invisibility in the secret data, while other applications need a larger magic formula message to get hidden. Discrete Wavelet Modification (DWT) will probably be presented in details and improvements will be applied to give a proposed formula to solve the condition of the extended processing period required for the present DWT techniques.

The paper shown in five sections. Section I shows the introduction with this paper. Section II covers the problem examination. Section 3 explains the proposed method. Section 4 shows the implementation and results. Finally section Versus gives the summary of this conventional paper.

PROBLEM ANALYSIS

In image centered steganography, it really is required for the steganography way to be able to conceal more key message pieces as possible in an image in a fashion that will not impact the two essential requirements which can be essential for the success of the concealing process:

Security/Imperceptibility: which means that human eye cannot separate the original photo and the stego image.

Capacity: this means the amount of magic formula data that can be embedded within a cover multimedia.

The relationship between the two requirements ought to be balanced, in other words if we raise the capacity higher than a certain limit then the imperceptibility will be damaged and so on, and so the digital steganography parameters needs to be chosen cautiously.

Both the DCT as well as the DWT methods come beneath transform domain name analysis and therefore are the most common nowadays. Both the strategies have good imperceptibility and in addition Robustness against statistical disorders. But as we know the major purpose of the steganography is to boost the robustness against attacks and to increase the payload capacity. In the case of capacity and processing period, DWT great compared to DCT.

The DWT is very suitable to identify the two significant regions in the images, Region of Interest (ROI) and Region of noninterest (RONI). Areas that contains most crucial information/data, basically the diagnostic component in case of radiographic images, is referred to as the RETURN which has almost all of the image energy and known as lower regularity sub-band (LL). So embedding the secret concept in (LL) sub- groups may weaken the image quality significantly. Although, the higher frequency sub-bands (HH), (LH) and (HL) includes the corners and smoothness of the photo that categorized as (RONI) at which a secret message can be inlayed effectively as well as the human eye is less sensitive to changes in this sort of sub-bands. This allows secret meaning to be stuck without being recognized by the eye so the majority of the steganographic methods use RONI for data embedding.

Another issue is that throughout the embedding process of the secret picture into the cover image the embedding and extraction procedure time differs with the key image size. The time increases with the size. This problem has been solved throughout the proposed formula.

PROPOSED TECHNIQUE

The proposed approach depends on applying the DWT on several stages instead of implementing this at once. This kind of economizes the processing period whereas the whole embedding time is less than the embedding time of the one stage embedding method.

Embedding Formula

The proposed approach to the sneaking in algorithm provides the following five modules.

• Dividing
• Applying DWT
• Embedding process
• Applying IDWT
• Merging

At module 1 the splitting method splits both of the cover and the secret images in four similar size sub-images each of them has a quarter with the original photo size. By module a couple of DWT is usually applied on each of the cover and secret sub-images (eight sub-images). At component 3 each one of the transformed top secret sub-images can be embedded in to the cover converted sub-images and it effects the stego sub-images (four sub-images). By module some IDWT is applied on each of the stego sub-images. At component 5 the ultimate stego image is built by merging the 4 sub-images as one image.

By the same manner the extraction formula has the same sequence with the embedding protocol except by module three or more the removal process is applied. By module 1 the breaking process splits the stego image in to four equivalent size sub-images each of them provides a quarter from the stego image size. By module two DWT is applied on all the stego image sub-images (four sub-images). In module 3 the removal process is usually applied to the stego photo sub-images to extract the key sub-images. By module 4 IDWT can be applied on all the secret sub-images. At module 5 a final secret photo is built by blending the four sub-images as one image.

The overall performance of the recommended algorithm was evaluated by three standard techniques. The Mean Sq Error (MSE) and the Weighted Peak Signal to Sound Ratio (WPSNR) which are used to measure the contortion between the unique cover graphic and the stego image following embedding the key information inside the cover. And also by the normalized cross correlation coefficient measure (NCCC) which is often used to gauge the similarity involving the cover plus the stego image.

Indicate Square Error (MSE)

MSE= 1/(M*N) ‘_(J=1)^(M-1)’‘_(K=1)^(N-1)’〖(X_(J, K)-X_(J, K)^)〗^2 (1)

Although X_(J, K) is the cover image that contains M*N pixels and X_(J, K)^ is a stego picture.

Normalized Cross Correlation Coefficient (NCCC) is given by (2).

NCCC= ‘_(J=1)^M’〖‘_(K=1)^N’〖(X_(J, K ). X_(J, K)^ 〗)1/(‘_(J=1)^M’‘_(K=1)^N’〖〖(X〗_(J, K))〗^2 )〗 (2)

NCCC may measure similarity up to several amount. The bigger NCCC benefit between the cover and the stego image signifies the higher performance of the technique.

Weighted Peak Transmission to Sound Ratio (WPSNR) is given by simply (3).

WPSNR=10log_10 〖(L_max/(RMSE ×NVF))〗^2 (3)

, NVF(i, j)=1/(1+θσ_x^2 (i, j))

The WPSNR uses an extra parameter called the Noises Visibility Function (NVF) which is a texture hiding function. The WPSNR uses the value of NVF as a penalization factor. Exactly where σ_x^2 (i, j) means the local difference of the graphic in a window centered on the pixel with coordinates (i, j) and θ is known as a tuning parameter corresponding for the particular photo. For flat regions, the NVF is close to 1 ) While for advantage or textured regions NVF is more near 0. This means that for clean image, WPSNR approximately equals to PSNR. But for textured picture, WPSNR is a little bit greater than PSNR.

BOTTOM LINE

A novel steganography algorithm have been proposed and a comparison study given the existing techniques. The suggested method generates no statistical or visual changes in the images, fast, and secure system for the telemedicine applications. The algorithm is vigorous against to any attacks. Rendering results revealed that the suggested method gives high quality images and requires a lot less processing period than past techniques. The algorithm is suitable for every kind of images. Further the system could be applied to several types of files just like Audio, Online video etc .

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