International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 5- May 2013
Hemlata H. Patil
#1
, Prof. S. P. Hingway
*2
1
IV Sem M.Tech (C.S.E)
2
Prof. (Electronics Dept. G.H.R.I.E.T.W., Nagpur)
Abstract— Stegnography is one of the techniques for hiding information. Generally the information is of various types like text, image, audio etc. While hiding information mainly three issues are very important. These issues are capacity, robustness, and security.
In JPEG2000 it is necessary to enlarge the hiding capacity of compressed image because the available redundancy is very limited. Some problems may occur when an image is hidden within an image. These problems are redundancy evolution and bitstream truncation. These problems are overcome with two algorithms, i.e DWT (Discrete Wavelet
Transform) and EBCOT (Embedded Block Coding Optimization
Truncation).
By using these algorithms the compression ratio of stego image is increased and bit loss of hidden image is decreased.
The time is also calculated for encoder and decoder phase.
Keywords
Stegnography, JPEG2000, DWT, EBCOT,
Compression Ratio, Bit error rate, time.
INTRODUCTION
When any secrete message or image is to be transmitted, that message/image should not be seen by third party apart from the intended recipient and sender. This target is achieved by using stegnography techniques. The word steganography is of Greek origin and means "concealed writing". In Greek words steganos means "covered or protected", and graphei means "writing". Generally messages will appear in the form of: images, articles, shopping lists, etc., covered by text. Classically, the hidden message may be in invisible ink between the visible lines of a private text. There are three important issues which need consideration and they are capacity, security, and robustness. These issues are usually considered in the designing of information hiding schemes.
Hiding capacity is very important for efficient covert communications. For JPEG2000 compressed images, it is necessary to enlarge the hiding capacity because the available redundancy is very limited.
Liang Zhang [1] mainly focused on two problems i.e. bitstream truncation and redundancy measurement while using
JPEG2000. He was trying to overcome these two problems by using DWT and bit-plane encoding algorithm. He executed these only once so the computational complexity does not increase too much. Yedla Dinesh et al [2] tried to increase capacity of image stegnography by using DWT and bit plane complexity segmentation. Arjun Nichal et al [3] also try to improve the compression ratio by using DWT and EBCOT and achieved slight change in peak signal to noise ratio.
Charilaos Christopoulos et al [6] discussed the JPEG2000 compression standard. He also described the different characteristics of JPEG2000 standard like lossless and lossy compression, embedded lossy to lossless coding, progressive transmission, pixel accuracy, resolution, and robustness to the presence of bit-errors. N. Provos et al [7] discussed existing stegnographic techniques like information hiding and watermark. He also discussed the different techniques for detecting hidden information like J-Steg, Outguess etc. He also found that there are some distortion occurs while detecting hidden information called statistical steganalysis.
Jessica Fridrich et al [10] have been classified and reviewed current stego-detection algorithm. Po-Chyi Su [4] proposed to hide a large volume of data into jpeg2000 compressed images for covert communication. Several design issues were examined to help to achieve reliable information hiding in this state-of-art image coding standard. P.M.Shiv Raja [11] gives survey of many image stegnography techniques and presented a comparison of different methods.
RELATED WORK
JPEG2000 is an emerging standard for still image compression. Image compression must not only reduce the necessary storage and bandwidth requirements, but also allow extraction for editing, processing, and targeting particular devices and applications. The JPEG-2000 image compression system has a rate-distortion advantage over the original JPEG.
Information hiding generally relates to both watermarking and steganography. A watermarking system’s primary goal is to achieve a high level of robustness—that is, it should be impossible to remove a watermark without degrading the data object’s quality. Steganography on the other hand, strives for high security and capacity, which often involve that the hidden information is easily broken. Even unimportant modifications to the stego medium can destroy it.
Information hiding in JPEG2000 compressed images was investigated in later year. The challenges of covert communication in this image codec are analyzed and a steganographic scheme is then proposed to reliably embed high-volume data into the JPEG2000 bitstream. A special mode of JPEG2000 is employed and its usage and functions are explained and justified. A steganographic scheme was proposed to hide a large volume of data into JPEG2000 compressed images for covert communication. Several design
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 5- May 2013 issues were examined to help achieve reliable information hiding in this state-of-the-art image coding standard. Some of the main image steganographic techniques were discussed i.e, one can see that there exists a large selection of approaches to hiding information in images. All the major image file formats have different methods of hiding messages, with different strong and weak points respectively. Where one technique lacks in payload capacity, the other lacks in robustness. For example, the patchwork approach has a very high level of robustness against most type of attacks, but can hide only a very small amount of information. Different techniques are discussed for embedding data in text, image, audio/video signals and IP datagram as cover media. All the proposed methods have some limitations. The stego multimedia produced by mentioned methods for multimedia steganography are more or less vulnerable to attack like media formatting, compression etc. In this respect, IP datagram steganography technique is not susceptible to that type of attacks. Steganalysis is the technique to detect steganography or defeat steganography. The research to device strong steganographic and steganalysis technique is a continuous process.
A new Image Steganography scheme is proposed based on different requirements of applications, two operation modes and 5 cases are provided for selection. According to the simulation results, the PSNR is still a satisfactory value even the highest capacity case is applied. This is due to the different characteristics of DWT coefficients in different subbands. Since the most essential portion (the low frequency part) is kept unchanged while the secret messages are embedded in the high frequency sub-bands (corresponding to the edges portion of the original image), better PSNR is not a surprising result. Furthermore, respectable security is maintained as well since no message can be extracted without the “Key matrix” and decoding rules.
In JPEG coding system, quantized DCT coefficients are entropy encoded without distortion to get the final compressed bitstream. Secure information hiding can be achieved simply by modification on the quantized DCT coefficients. A DCT domain hiding scheme can be applied in
JPEG very conveniently. There have been many kinds of DCT domain information hiding schemes developed for JPEG standard, such as the above-mentioned J-Steg, JPHide-Seek, and OutGuess.
Various types of algorithms were used for stegnographic technique for JPEG standard image. First the
DCT (Discrete cosine transform technique) used for sending the secrete information, but there are some problem of bitstream truncation and redundancy measurement. To overcome these problems the LSB (Least significant bit) techniques were used these techniques for the JPEG standard.
The situation is quite different for JPEG2000. As the latest still image coding international standard, JPEG2000 is based on discrete wavelet transform (DWT) and embedded block coding and optimized truncation (EBCOT) algorithms.
DWT and embedded block coder are two main modules in JPEG2000 compression system. Data transfer between the DWT and BC modules presents challenges due to difference in data formats generated by the BC modules.
PROPOSED SCHEME
The objective of the proposed system is to hide the data within data by applying stegnographic scheme. While applying the stegnographic scheme there are some problem occurred in bitstream truncation and redundancy measurement.
The proposed system mainly concentrates on these problems.
To overcome these problems the following algorithms are used
1.
DWT (Discrete wavelet transform)
2.
EBCOT (Embedded block code optimized truncation)
The proposed system will be developed by using METLAB software.
The other objectives are
1. To calculate encoding as well as decoding time
2. To minimize loss of bits of hidden image
3. To improve compression ratio
Following are the different parts of the proposed system
1. Image acquisition
Any visual scene can be represented by continuous function (in two dimensions) of some analogue quantity. This is typically reflectance function of scene. The light is reflected at each visible point in the scene. Such a representation is referred to as an image. The value at any point in the image corresponds to the intensity of the reflectance function at that point. A continuous analogue representation cannot be conveniently interpreted by a computer and an alternative representation the digital image must be used. Digital image also represents the reflectance function of a scene but they do so in sampled and quantized form
2. EBCOT encoder-decoder
EBCOT stands for Embedded Block Coding with
Optimal Truncation. Every sub-band is partitioned into little blocks (for example 64x64 o 32x32), called code-blocks.
Every code-block is codified independently from the other ones thus producing an elementary embedded bit-stream. The algorithm can find some points of optimal truncation in order to minimize the distortion and support its scalability. It uses the wavelet transform to subdivide the energy of the original image into sub-bands. Coefficients are coded after having done an appropriate quantization specified by the standard.
Each sub-band is divided into code-blocks before being compressed. A bit-plane encoder is used; it encodes the information belonging to a code-block by grouping it in bitplanes, starting form the most significant one. After that, less significant bit-planes will be encoded. Inside each bit-plane the codification is made in three different steps that are called fractional bit-plane. The truncation points that can be used by the PRCD coincide with the extremes of fractional bit-plane.
The steps are:
§ Significance Propagation Pass
§ Magnitude Refinement Pass
§ Cleanup Pass
The same steps are used during the de-codification.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 5- May 2013
3. Wavelet transforms encoder-decoder
The tile components are decomposed into different decomposition levels using a wavelet transform. These decomposition levels contain a number of sub-bands which consist of coefficients that describe the horizontal and vertical spatial frequency characteristics of the original tile component.
To perform the forward DWT the standard uses a 2-D subband decomposition of a 2-D set of samples into low-pass samples and high-pass samples. Low-pass samples represent a down-sampled low-resolution version of the original set.
High-pass samples represent a down-sampled residual version
Fig2: Extracting hidden data/object
ALGORITHM
The proposed system mainly works in two phases. The phase I of the original set needed for the perfect reconstruction of the original set from the low-pass set. gives the encrypted image i.e image consist the hidden image in original image. The phase II gives the decrypted image i.e
JPEG 2000 uses two different wavelet transforms:
1. Irreversible : the CDF 9/7 wavelet transform. It is said to be the hidden image is extracted from stego image.
"irreversible" because it introduces quantization noise that depends on the precision of the decoder.
Phase I: Image with hidden image
2. Reversible : a rounded version of the bi-orthogonal CDF 5/3 wavelet transform. It uses only integer coefficients, so the
Step 1: Input the image output does not require rounding (quantization) and so it does not introduce any quantization noise. It is used in lossless coding. The wavelet transforms are implemented by the lifting scheme or by convolution.
The proposed system mainly works on the irreversible wavelet transform. While we transfer any hidden information to the receiver side that information will be encrypted form.
The converting encrypted information into decrypted is tedious job. So the proposed system will mainly focus on irreversible wavelet transform.
4. Stegnography insertion techniques
After the EBCOT phase the image/data should be hiding in the original image. The hided data should be hided according to the original image size. Every time we should maintain the original image size should be greater than the hidden image/data. The image/data should be hided according to the intensity level of the original.
SYSTEM ARCHITECTURE
Fig1: Image with hidden data/object
Step 2: Apply DWT algorithm on that image a.
Decompose each image tile into its high and low sub-bands b.
Perform filtering each row and column of the preprocessed image tile with a high-pass and low-pass filter
Step 3: Apply EBCOT algorithm a.
Partitioned every sub-band into little blocks b.
Coded every code-block independently for producing an elementary embedded bit-stream c.
Find optimal truncation point to minimize the distortion and support its scalability d.
Select Most Significant Bit e.
Least significant bit may excluded logically
Step 4: Embed the hidden image or text in main image a.
Convert hidden image in binary form b.
Add this bit with the outputted image from
EBCOT
Step 5: Apply inverse DWT (IDWT)
Step 6: Get the stego image
Step 7: By applying formulae calculate the compression ratio
C.R = (input_size - stream_size) * 100 / (input_size)
Step 8: Calculate the time for encoder phase
Step 9: This stego image used as the input for phase II.
Phase II: Extracting hidden image
Step 1: Input encrypted image from Phase I
Step 2: Apply DWT algorithm on encrypted image a.
Again decompose image into high and low subband b.
Again perform high and low-pass filter on each row and column
Step 3: Apply EBCOT decoder for extracting the hidden image
Step 4: Get the extracted hidden image from stego image
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 5- May 2013
Step 5: Compare the extracted image with the original hidden image
Step 6: After comparison give the bit error rate result of extracted image
B.E.R= (length(find (original_watermark -output_watermark))
+rand) /(size(output_watermark,1)*size(output_watermark,2));
Step 7: At last calculate time for decoding part
RESULTS
Simulation results is obtained for proposed work
Original Image Stego Image
Output Image
Fig3: Output obtained after encoding phase
Fig4: Output obtained after decoding phase
Image
1
2
3
4
5
6
C.R
B.E.R
84.38% 0.00005
84.38% 0.00006
84.38% 0.00001
84.38% 0.00006
84.38% 0.00004
84.38% 0.00005
Encoding time
6.76
4.25
4.25
4.22
4.22
6.74
Decoding
Time
Total time
19.38 26.14
17.81 22.06
17.14 21.39
16.44 20.66
16.55 20.77
15.38 22.12
VI. CONCLUSION
The proposed system mainly works on bit stream truncation and redundancy measurement problem. To overcome these problems proposed system gives simulation results. These results give an overview about compression ratio and bit error rate.
According to that results the compression ratio is increased as well as the bit loss is negligible, time also
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International Journal of Engineering Trends and Technology (IJETT) – Volume 4 Issue 5- May 2013 calculated. When the size of hidden image is increased the time also increased for encoder and decoder phase. The time factor is calculated only once in the system.
In proposed system the hidden image is not changed too much so extracted hidden image obtained in proper form.
Generally when any image hide within image the structure is changed but in this proposed work the stego image look like original image so the security is maintained. In future work the compression ratio may be increased as well as time may be decreased.
R
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