Digital Image Steganography Based on Combination of
DCT and DWT
Vijay Kumar1 and Dinesh Kumar2
1
CSE Department, JCDMCOE, Sirsa, Haryana, India
vijaykumarchahar@gmail.com
2
CSE Department, GJUS&T, Hisar, Haryana, India
dinesh_chutani@yahoo.com
Abstract. In this paper, a copyright protection scheme that combines the
Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is
proposed. The proposed scheme first extracts the DCT coefficients of secret
image by applying DCT. After that, image features are extracted from cover
image and from DCT coefficients by applying DWT on both separately. Hiding
of extracted features of DCT coefficients in the features of cover image is done
using two different secret keys. Experimentation has been done using eight
different attacks. Experimental results demonstrate that combining the two
transforms improves the performance of the steganography technique in terms
of PSNR value and the performance is better as compared to that achieved
using DWT transform only. The extracted image has good visual quality also.
Keywords: Discrete Cosine Transform, Discrete Wavelet Transform,
Steganography.
1 Introduction
The rapid growth of multimedia processing and Internet technologies in recent years
has made it possible to distribute and exchange huge amount of multimedia data more
easily and quickly than ever at low cost. The data can be easily edited with almost
negligible loss using multimedia processing techniques. Therefore, the need for the
copyright protection of digital data has emerged. Nowadays, Steganography has
become the focus of research for copyright protection.
There are two approaches related to steganography i.e., spatial-domain and
frequency-domain approach [13]. In the former approach, the secret messages are
embedded into least significant pixels of cover images. They are fast but sensitive to
image processing attacks. The latter approach contains transforming the cover image
into the frequency domain coefficients before embedding secret messages in it. The
transformation can be either Discrete Cosine transform (DCT) or Discrete Wavelet
Transform (DWT) etc. Though these methods are more difficult and slower than
spatial domain, yet they have an advantage of being more secure and noise tolerant
[12]. Among these methods, DWT has been widely used in digital image
steganography due to its multi-resolution characteristics.
V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 596–601, 2010.
© Springer-Verlag Berlin Heidelberg 2010
Digital Image Steganography Based on Combination of DCT and DWT
597
In this paper, steganography based on combination of two transforms DWT and
DCT has been described. The proposed technique showed high robustness against
many image processing attacks. The remainder of this paper is organized as follows.
Section 2 presents the related work. Section 3 presents the proposed DCT-DWT based
digital image steganography approach. Section 4 shows the experimentation and
results followed by conclusions in section 5.
2 Related Work
Least Significant Bit Substitution (LSB) [1] is the most commonly used
steganography technique. Sinha and Singh [2] proposed a technique to encrypt an
image for secured transmission using digital signature of the image. Digital signatures
enable the recipient of a message to verify the sender of a message and validate that
the message is intact. In [3], a spatial domain approach, the authors proposed the
exploitation of correlation between adjoining pixels for determining the bit number to
be embedded at certain specific pixel.
In [4], a frequency domain approach, the authors proposed that embedding is
realized in bit planes of subband wavelet coefficients obtained by using the Integer
Wavelet Transform. In [5], authors proposed an algorithm that utilized the probability
density function to generate discriminator features fed into a neural network system
which detected hidden data in this domain. Tsuang-Yuan et al. [6] proposed a new
method for data hiding in Microsoft word documents by a change tracking technique.
Kisik et al. [7] proposed a stegnographic algorithm which embeded a secret message
into bitmap images and palette-based images. The algorithm divided a bitmap image
into bit plane images from LSB-plane to MSB-plane for each pixel. Satish et al. [8]
proposed a chaos based spread spectrum image steganography method. The majority
of LSB steganography algorithm embed message in spatial domain such as pixel
value differencing [9].
McKeon [10] proposed a methodology for steganography based on fourier domain
of an image by using the properties of zero-padding. These zeros can be changed
slightly where the change in the image is not noticeable. In [11], authors discussed the
effects of steganography in different image formats and DWT. They also introduced
the number of payload bits and the place to embed. In [14], authors proposed method
to spread hidden information within encrypted image data randomly based on the
secret key. Li et al. [17] proposed loseless data hiding using difference value of
adjacent pixels instead of the whole image. Tsai et al. [15] divide the image into
blocks where redual image was calculated using linear prediction. Then, the secret
data was embedded into the residual values, followed by block reconstruction. Chao
et al. [16] presented the embedding scheme that hide secret messages in the vertices
of 3D polygen models.
3 DWT-DCT Based Digital Image Steganography Approach
In this paper, we combine the algorithm [12] with Discrete Cosine Transform (DCT).
The proposed algorithm is as follows.
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V. Kumar and D. Kumar
3.1 Embedding Procedure
The steps of embedding procedure are as follows:
1.
2.
3.
Apply DCT to the secret image S to get DCT coefficients.
Decompose the cover image (I matrix) and the DCT coefficients of secret image
into four sub-images (ICA, ICH, ICV, ICD) and (CCA, CCH, CCV, CCD)
respectively using DWT.
Each of CCA, ICA, and ICH are partitioned into blocks of 4 × 4 pixels and can be
represented by:
CCA = {BS i ,1 ≤ i ≤ ns}
(1)
ICA = {BC j ,1 ≤ j ≤ nc}
(2)
ICH = {BH k ,1 ≤ k ≤ nc}
(3)
where BS i , BC j ,and BHk represent the i
th
block in CCA, the j
th
block in
th
4.
ICA and the k
block in ICH respectively. ns is the total number of the
4 × 4 blocks in CCA and nc is the total number of the 4 × 4 blocks in each of
ICA and ICH.
For each block BS i in CCA, the best matched block BC j of minimum error in
5.
ICA is searched by using the root mean squared error (RMSE).The first secret
key K1 consists of the addresses j of the best matched blocks in ICA.
Calculate the error block EBi between BS i and BC j as follows:
EBi = BC j − BS i
6.
For each error block EBi , the best matched block
using the RMSE criteria as before and that
7.
8.
(4)
BH k in ICH is searched for
BH k is replaced with the error
block EBi . The second secret key K2 consists of the addresses k of the best
matched blocks in ICH.
Repeat the steps 4 to 6 until all the produced error blocks are embedded in ICH.
Apply the inverse DWT to the ICA, ICV, ICD, and the modified sub-image ICH
to obtain the stegano-image G.
3.2 Extraction Procedure
The steps of secret image extraction procedure are as follows:
1.
Decompose the stegano-image G into four sub-images (GCA, GCH, GCV, GCD)
using DWT transform.
Digital Image Steganography Based on Combination of DCT and DWT
2.
599
Extract the block BC j from the sub-image GCA by using the first secret key
K1. Use the second secret key K2 to extract the error blocks. The secret
blocks BS i can be obtained by:
BS i = BC j − EBi
3.
4.
5.
(5)
Repeat step 2 until all the secret blocks are extracted and form the sub-image
CCA.
Using detail coefficients from sender such as CCD, CCV, CCH and extracted
CCA from above step, apply the inverse DWT to obtain the DCT coefficients.
Apply the inverse DCT on DCT coefficients obtained from step 4.
4 Experimentation and Results
4.1 Experiment 1 and Results
We evaluate the performance of the combined DCT-DWT based digital image
steganography using four cover images: Peppers, Lena, Goldhill and Boat, each of
size 256 × 256 and four secret images: Redfort, Watch, C.V. Raman and Taj Mahal,
each of size 128 × 128. Figure 1 shows all the secret images.
(a)
(b)
(c)
(d)
Fig. 1. Secret images (a) Redfort (b) Watch (c) Raman (d) Taj Mahal
We compare the two techniques, LSB [1] and Ahmed A. Abdelwahab [12] with
proposed method using above mentioned four cover images and Redfort as secret
image. The PSNR values of stegano-images after embedding secret image for above
said methods are tabulated in table 1. The results reveal that proposed method has
higher PSNR value than the other two methods.
Table 1. Comparison between LSB [1], Ahmed [12] and Proposed methods in terms of PSNR
using Redfort as secret image
Image
Cover Image
(256x256)
Peppers
Lena
Goldhill
Boat
LSB[1]
PSNR
Ahmed[12]
10.75
09.66
11.16
12.91
31.59
31.86
31.86
32.37
Proposed
method
42.09
41.93
41.84
42.45
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V. Kumar and D. Kumar
4.2 Experiment 2 and Results
The next experiment was performed to see the effect of chosen attacks such as
Gaussian Noise, Sharpening, Median Filtering, Gaussian Blur, Histogram
Equalization, Gamma Correction, Transform and Cropping. The PSNR values for
four different stegano images and extracted secret images after different image
processing attacks are illustrated in table 2. The results reveal that after applying
attacks on stegano images, the secret images have high value of PSNR and hence the
good visual quality.
Table 2. PSNR of stegano-images and extracted secret images under different image
processinh attacks
Image
G.
Noise
Stegano-Peppers 19.72
Extract-Redfort
19.50
Stegano-Lena
19.89
Extract-Watch
19.37
Stegano-Goldhill 18.69
Extract-Raman
18.66
Stegano-Boat
19.61
Extract-Taj
15.52
Sharp.
17.15
17.10
13.65
31.01
11.95
31.64
16.10
22.05
PSNR with different attacks
Hist.
G. Blur Gamm. TransEqul.
Corr.
form
20.01
26.19
52.80
11.75
13.26
25.36
12.44
38.92
16.25
25.42
40.48
11.78
27.08
37.65
36.34
40.03
16.51
25.06
25.54
12.61
30.06
32.68
20.19
38.66
16.16
19.61
38.42
13.26
20.12
15.52
12.35
38.23
Median
Filter.
27.61
23.32
25.37
37.27
24.87
32.38
26.63
21.19
Crop.
08.69
09.51
08.70
30.64
10.07
16.97
08.83
15.84
5 Conclusion
This paper presented a digital image steganography technique in which DCT was
combined with DWT. The experimentation was done using different attacks. The
simulation results depict that there is substantial increase in the PSNR value of the
stegano images. Further a comparison of proposed technique with the earlier existing
techniques [1] and [12] establishes supremacy of the proposed algorithm.
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