Data Steganography for Optical Color Image Cryptosystems

Cheng-Hung Chuang & Guo-Shiang Lin
International Journal of Image Processing Volume (3): Issue (6) 318
Data Steganography for Optical Color Image Cryptosystems
Cheng-Hung Chuang chchuang@asia.edu.tw
Department of Computer Science and Information Engineering
Asia University
Taichung County, 41354, Taiwan
Guo-Shiang Lin khlin@mail.dyu.edu.tw
Department of Computer Science and Information Engineering
Da-Yeh University
Changhua County, 51591, Taiwan
Abstract
In this paper, an optical color image cryptosystem with a data hiding scheme is
proposed. In the proposed optical cryptosystem, a confidential color image is
embedded into the host image of the same size. Then the stego-image is
encrypted by using the double random phase encoding algorithm. The seeds to
generate random phase data are hidden in the encrypted stego-image by a
content-dependent and low distortion data embedding technique. The
confidential image and secret data delivery is accomplished by hiding the image
into the host image and embedding the data into the encrypted stego-image.
Experimental results show that the proposed data steganographic cryptosystem
provides large data hiding capacity and high reconstructed image quality.
Keywords: Data embedding, Data hiding, Image encryption, Optical security, Double random phase.
1. INTRODUCTION
With the fast development of communication and network technology, it is convenient to acquire
various multimedia data through Internet. Unfortunately, the problem of illegal data access
occurred frequently and popularly. Hence, it is important to protect the content and the authorized
use of multimedia data against the pirates. Data encryption is a strategy to make the data
unreadable, invisible or incomprehensible during transmission by scrambling the content of data
[1]. In an image cryptosystem, it uses some reliable encryption algorithms or secret keys to
transform or encrypt secret images into ciphered images. Only the authorized users can decrypt
secret images from the ciphered images. The ciphered images are meaningless and non-
recognizable for any unauthorized users who grab them without knowing the decryption
algorithms or the secret keys.
Dissimilarly, data hiding or steganographic techniques refer to methods of embedding secret data
into some host data in such a way that people can not discern the existence of the hidden data.
For example, the well-known watermarking which usually hides copyright marks in multimedia
data is a kind of data hiding technique [2]. Common methods for data hiding can be categorized
into spatial and transform domain methods. The earliest method, which is simple and has high
embedding capacity, embedded data into least significant bits (LSBs) of image pixels (i.e. spatial
domain). Contrarily, in the transform domain, e.g., discrete cosine transform (DCT), Fourier
transform, or wavelets, transformed coefficients of host signals can be manipulated to hide

messages. The image steganographic methods (or called virtual image cryptosystems) [3-6] are
proposed to hide the secret images into readable but non-critical host images. They are designed
to reduce the notice of illegal users.
For high speed application, image encryption methods based on optical systems have been
developed. Many optical image encryption algorithms have been proposed for transmission
security [7-11]. The double random phase encoding [7] is a famous and widely used algorithm
which employs two random phase masks in the input plane and the Fourier plane to encrypt
images into stationary white noise. In [8], an optical image cryptosystem based on the double
random phase encryption and a public-key type of data embedded technique is proposed. In [9],
a new image cryptosystem with an adaptive steganographic method is proposed to improve the
security and visual quality. However, the input image is limited to grayscale in those
cryptosystems. In [10], the encryption method using wavelength multiplexing and lensless Fresnel
transform hologram is proposed for color image application. In [11], the optical color image
encryption scheme is performed in the fractional Fourier transform domain.
In this paper, we propose a data steganographic scheme within an optical color image
cryptosystem. A confidential color image is embedded into the phase term of the host image to
become the stego-image. Then it is encrypted by using the double random phase algorithm, that
is, it is multiplied by two random phase masks. The seeds to generate random phase data are
embedded into the LSBs of the encrypted stego-image, in which a zero-LSB sorting technique is
applied to find the hiding sequence. Simulations and experiments regarding the hiding method (in
comparison with the traditional scheme [8]) are performed. Experimental results show that the
proposed color image cryptosystem has a good performance in secure data embedding, large
hiding capacity, and high visual quality.
In Section 2, the conventional data embedding technique and the optical cryptosystem are
reviewed. Section 3 introduces the proposed steganographic optical color image cryptosystem.
Section 4 shows some experimental results to demonstrate the performance of the proposed
scheme and a comparison with the previous method [8]. Finally, Section 5 gives conclusion and
future work.
2. REVIEW OF OPTICAL IMAGE CRYPTOSYSTEM
In optical image cryptosystems, the double random phase algorithm [7] is a very common
encryption and decryption method. In the double-random-phase encoding, an image I is
multiplied by a random phase mask 1P in the input spatial plane and Fourier transformed to
frequency domain. It is multiplied by another random phase mask 2P in the Fourier plane. Then it
is inverse Fourier transformed to obtain its ciphered image EI in the output spatial plane. In the
decoding process, the ciphered image EI is Fourier transformed and then multiplied by the
conjugate function of mask 2P and inverse Fourier transformed to spatial domain. It is multiplied
by the conjugate function of mask 1P to obtain its deciphered image DI in the output spatial
plane. The equations are expressed as follows.
( )[ ]21
1
PPIFFIE ××= −
(1)
( )[ ] *
1
*
2
1
PPIFFI ED ××= −
(2)
where )2exp( 11 piP π= and )2exp( 22 piP π= , 1p and 2p are random numbers of the image size
between [0, 1], F and 1−
F define the Fourier and inverse Fourier transforms, and * denotes the
conjugate operation. The optical 4f architecture, where f is the focal length of the lens, is shown in
Figure 1.

(a)
(b)
FIGURE 1: Optical 4f Architecture of the image cryptosystem. (a) Encryption (b) Decryption.
The data hiding scheme for the optical image cryptosystem proposed in [8] always embeds data
in a fixed area of the encrypted image. Although it is a simple and fast way to complete the data
embedding and extracting framework, the visual quality of the decrypted images is lower when
the hidden data size is large. Therefore, in [9], a new image cryptosystem with an adaptive
steganographic method is proposed to improve the visual quality of the reconstructed images. In
this paper, the adaptive data hiding method is applied to the proposed optical color image
cryptosystem for embedding the seeds which are used to generate double random phase.
Besides, confidential or secret images can be embedded into the host images in the proposed
cryptosystem.
3. THE PROPOSED METHOD
The proposed optical color image cryptosystem is based on the double random phase encryption
theorem [7]. Before encoding, the confidential image cI is embedded into the phase term of the
host image hI . Then the stego-image sI is multiplied by a random phase mask 1P in the input
domain and transformed to Fourier plane. It is multiplied by another random phase mask 2P and
converted to the spatial domain for obtaining the encrypted stego-image eI . The equations are
defined as follows.
)
2
exp( chs IiII
π
= (3)
( )[ ]21
1
PPIFFI se ××= −
(4)
where )2exp( 11 piP π= and )2exp( 22 piP π= , 1p and 2p are random numbers of the image size
between [0, 1], and F and 1−
F define the Fourier and inverse Fourier transforms.
In the decoding step, the encrypted stego-image eI is transformed to the Fourier plane, multiplied
by the conjugate of mask 2P , converted to spatial domain, and multiplied by the conjugate of
mask 1P to obtain its decrypted image dI . Ideally, the decrypted image dI is equal to the stego-

image sI in a lossless manner. The host image can be obtained by computing the complex
modulus of the decrypted image dI . Also the secret image can be retrieved by calculating the
complex argument of the decrypted image dI . The equations are described as follows.
( )[ ] *
1
*
2
1
PPIFFI ed ××= −
(5)
dh II = (6)
2/
)arg(
π
d
c
I
I = (7)
where *
1P and *
2P indicate the conjugate masks of 1P and 2P , and )arg(⋅ takes the complex
argument.
For color images, they are first separated into three channels: red, green, and blue. Each channel
is processed from Equations (3) to (7). However, the three channels can be coded by different
random phase masks, i.e. multiplied by random phase masks RP1 , GP1 , BP1 and RP2 , GP2 , BP2 .
The seeds to generate random phase data are embedded into the encrypted stego-image eI . In
the receiver side, the first thing is to decode the embedded seeds from the encrypted stego-
image eI . The decoded seeds are used to re-generate the same random numbers which are
applied to produce the conjugate random phase masks *
1RP , *
1GP , *
1BP and *
2RP , *
2GP , *
2BP . Thus
one can decode the encrypted stego-image eI to get the decrypted image dI using the hidden
seed data extracted from the encrypted stego-image itself. Figure 2 shows the schema of the
proposed optical color image cryptosystem.
(a)
XX
Random
phase masks
P2R*, P2G*, P2B*
Random
phase masks
P1R*, P1G*, P1B*
Host
image
Secret
image
Decrypted
Stego
imageEncrypted
Stego
image
Ic
Ih
Seeds
F-1
FE-1
Modulus
Argument
(b)
FIGURE 2: Schema of the proposed color image cryptosystem. (a) Encryption,
(b) Decryption (X: multiplication, E and E
-1
: the data embedding and extracting
functions, F and F
-1
: the Fourier and inverse Fourier transforms).
Since the signal values in the optical system are complex number format, there are real and
imaginary parts that can be used to embed data. In this paper, we choose the real parts of
complex numbers to be the hidden site. That is, the seeds for generating random phase data are
embedded into LSBs of the quantized real parts of the encrypted stego-image bit by bit. However,

the quantization and embedding procedure will cause the loss of visual quality in the decrypted
host and confidential images. The important issue is how to select the hidden positions that result
in low distortion of the decrypted images. It is a simple way to hide the data within a fixed region
in the encrypted stego-image [8]. Nevertheless, due to the different image content, the fixed
hidden positions are not always suitable for hiding data. To improve the visual quality of the
decrypted image and more safely convey the secret seed data, a low distortion, adaptive, and
content-dependent data hiding technique [9] is applied to hide the secret data. In our strategy, the
positions with smaller absolute values are preferable since they have smaller energy and
quantization step size. To keep the embedding and decoding sequences invariant, the LSBs are
set to zero and a sorting technique is employed. The detailed data hiding and extraction
procedures are described as follows.
3.1 Data Hiding Procedure
Step 1: Assume that there are N bits in the secret data },...,,{ 21 NbbbB = . The values of real parts
in the encrypted stego-image eI are sorted in ascending order with their absolute values. The
sorted set of the first N+2 numbers except the maximum and the minimum is chosen and defined
as =Λ },,,{ 21 Nααα Κ , where 1+≤ ii αα , iα and 1+iα Λ∈ . Note that the maximum and
minimum in the first N+2 numbers are not used to be quantized and hidden data because the
quantization step size is computed from them.
Step 2: The sorted set Λ is quantized to become },...,,{)( 21 qNqqLQ Q ααα=Λ=Λ , where (.)LQ
denotes a quantizer with L levels.
Step 3: The zero-LSB set },...,,{ 21 qzNqzqzQZ ααα=Λ is obtained by setting all LSBs of QΛ to be
zero. The elements in QZΛ are sorted in ascending order with their absolute values to get
},...,,{ 21 NqzsqzsqzsQZS ααα=Λ , where 1+
≤ ii qzsqzs αα , iqzsα and 1+iqzsα QZSΛ∈ .
Step 4: The sequence },...,,{ 21 NsssS = , where },...,2,1{ Nsi ∈ and Ni ,...,2,1= , generated by the
set QZSΛ , is used to be the data hiding index. That is, the secret data is successively embedded
into the LSBs of the set QΛ according to the sequence S, i.e. },...,,{ 21 NqsqsqsQS ααα=Λ , where
Qqsi
Λ∈α .
Step 5: The hiding rule is defined as
))2,mod(sgn( QSQS
E
QS B Λ−+Λ=Λ (8)
where }1,0,1{)sgn( −∈⋅ is the signum function and },...,,{ 21 NbbbB = is the secret data. The set with
hidden data is },...,,{ 21
e
qs
e
qs
e
qs
E
QS N
ααα=Λ .
Step 6: Finally, the set E
QSΛ is de-quantized to obtain )(1 E
QSL
E
S Q Λ=Λ −
},...,,{ 21
e
s
e
s
e
s N
ααα= , where
()⋅−1
LQ is the de-quantizer with L levels.
3.2 Data Extraction Procedure
Step 1: This step is the same as the first step in data hiding procedure to find the sorted set. The
set is defined as },...,,{ 21
e
N
eeE
ααα=Λ , where e
i
e
i 1+≤ αα , e
iα and e
i 1+α E
Λ∈ . The sequence in the
sorted set E
Λ is different from that in the sorted set Λ .
Step 2: The sorted set E
Λ is quantized with L levels to be ( )=Λ=Λ E
L
E
Q Q },...,,{ 21
e
qN
e
q
e
q ααα .
Step 3: All LSBs of E
QΛ are set to zero to obtain the zero-LSB set },...,,{ 21
e
qzN
e
qz
e
qz
E
QZ ααα=Λ . The
elements in E
QZΛ are sorted in ascending order with their absolute values to get =ΛE
QZS
},...,,{ 21
e
qzs
e
qzs
e
qzs N
ααα , where e
qzs
e
qzs ii 1+
≤ αα , E
QZS
e
qzs
e
qzs ii
Λ∈+1
,αα .

Step 4: Now, the set E
QZSΛ is equal to the set QZSΛ with the same sequence },...,,{ 21 NsssS = . The
hidden data is extracted from the LSBs of the set },...,,{ 21
e
qs
e
qs
e
qs
E
QS N
ααα=Λ , i.e.
Ni
b
b
e
qsi
e
qsi
i
i
,...,2,1,
1,2)mod(if,1
0,2)mod(if,0
=




==
==
α
α
(9)
4. EXPERIMENTAL RESULTS
In the experiment, one hundred 24-bit 512×512-pixel various color images (collected from [12-
14]) are examined as host images and the peak signal-to-noise ratio (PSNR) is applied to
evaluate the visual quality of the decrypted images. The equation is defined as follows.
3
BGR MSEMSEMSE
MSE
++
= (10)
MSE
PSNR
2
10
255
log10×= (11)
where MSER, MSEG, and MSEB are mean square errors in three channels, respectively.
The traditional data hiding scheme [8], where the secret data are embedded in the central square
area of the encrypted stego-image, is performed for comparison. For a fair evaluation, the size of
hidden data is fixed and set to 480,000 bits. Table 1 shows the average PSNR values of the 100
decrypted host images and the retrieved secret images with different quantization levels, i.e. L =
8, 16, 32, 64, 128, and 256. It is clear that the PSNR values increase about 14 dB both in the
decrypted host and secret images of the proposed method. Figure 3 plots detailed PSNR values
of 100 decrypted host and secret images with L = 8, where the blue and red curves are the
results of the proposed and the traditional methods, respectively. Figure 4 shows some original,
decrypted host, and decrypted secret images sampled from the 100 test cases, where the
quantization level is 8. The first row of Figure 4 shows the original images, where Figure 4(a)-(c)
are host images and Figure 4(d) is the secret image. The second row of Figure 4 is the results of
the proposed method, where the PSNR values are 19.14, 20.27, 20.22, and 32.10 dB,
respectively. The last row of Figure 4 is the results of the traditional data hiding method [8], where
the PSNR values are 4.81, 5.52, 5.79, and 16.96 dB, respectively.
Average PSNR (dB)
Proposed method Ref. [8]L
Host images Secret images Host images Secret images
8 20.88 32.14 6.57 16.89
16 26.88 38.19 12.56 23.70
32 32.89 44.22 18.57 29.85
64 38.91 50.24 24.57 35.89
128 44.93 56.26 30.59 41.91
256 50.95 62.28 36.64 47.97
TABLE 1: Comparisons between the proposed method and the traditional
scheme [8] of the average PSNR values of the 100 decrypted host images and
the retrieved secret images. (hidden data 480,000 bits)

FIGURE 3: PSNR curves of the 100 decrypted host and secret images. The blue
and red curves are the results of the proposed and the traditional methods [8],
respectively. (L = 8, hidden data 480,000 bits)
(a) (b) (c) (d)
(e) (f) (g) (h)
(i) (j) (k) (l)

FIGURE 4: (a)(b)(c) The original host images and (d) the secret image, (e)-(h)
the decrypted host and secret images by the proposed method, (i)-(l) the
decrypted host and secret images by the traditional data hiding scheme. (L = 8,
hidden data 480,000 bits)
To evaluate the data hiding capacity versus the visual quality of the decrypted host and secret
images, the encrypted stego-images are embedded with different data size ranged from 108 to
about 740,000 bits. The quantization level is set to 8. The average PSNR values of the 100
decrypted host and secret images are computed for evaluating the visual quality. Figure 5 shows
the curves of the data hiding capacity versus the average PSNR values, where the blue and red
curves are the results using the proposed and traditional methods, respectively. The PSNR
values in the results of the proposed method are larger than those in the results of the traditional
scheme when the sizes of hidden data are the same. It is obvious that the proposed method has
a better performance than the traditional one.
FIGURE 5: Curves of the data hiding capacity versus the visual quality of the
decrypted host and secret images. The blue and red curves are the results of the
proposed and the traditional methods [8], respectively. (L = 8)
In real applications, pirates may attempt to maintain the reconstructed images recognizable by
modifying the encrypted stego-images. However, when the encrypted stego-images are attacked,
it is expected that the hidden data will be altered. If the hidden data can not be correctly extracted,
the stego-images can not be properly decrypted. In this part of experiment, it is assumed that the
hidden data are completely cracked and the amplitude parts of the encrypted stego-images are
suffered from three common attacks, i.e. noising, smoothing, and JPEG compression. The
Gaussian noise (with zero mean and 0.01 variance) and the 3×3 averaging filter are exploited to
disturb the encrypted stego-images for the noising and smoothing attacks. In the JPEG
compression, 56.25% (36/64) of the DCT coefficients in the high frequency part in each 8×8 block
were discarded (set to zero). The size of hidden data is set to 480,000 bits. The quantization level
is also set to 8. The average PSNR values of the 100 decrypted host and secret images are
calculated and listed in Table 2, where the encrypted stego-images are suffered from attacks.
Without attacks, the average PSNR values in the decrypted host and secret images (shown in
Table 1) are 20.88 and 32.14 dB by the proposed method and 6.57 and 16.89 dB by the

traditional scheme, respectively. With the three attacks, the PSNR values of all the decrypted
host and secret images are reduced. The visual quality of the decrypted host and secret images
in the smoothing attack is almost the worst. However, the results of the proposed method are still
better than those of the traditional scheme.
Average PSNR (dB)
Proposed method Ref. [8]Three common attacks
Host images Secret images Host images Secret images
Noising 9.08 19.85 4.42 14.02
Smoothing 7.25 16.31 5.34 12.31
JPEG compression 9.06 19.12 5.69 13.71
TABLE 2: Comparisons between the proposed method and the traditional
scheme [8] of the average PSNR values of the 100 decrypted host images and
the retrieved secret images when the encrypted stego-images are attacked. (L =
8, hidden data 480,000 bits)
5. CONCLUSION & FUTURE WORK
In this paper, the optical color image cryptosystem with data steganography is proposed. The
double random phase encoding algorithm and the adaptive data hiding technique are applied in
the proposed color image cryptosystem. The confidential image is hidden in the phase term of the
host image. Then the stego-image is encrypted by the double random phase encoding algorithm.
The seeds to generate random phase data are embedded into the encrypted stego-image by the
proposed data hiding method. In comparison with the traditional hiding scheme, a larger data
embedding capacity and higher visual quality of the decrypted host and confidential images are
achieved.
For the advanced security, the confidential image and the secret data can be disordered by the
scrambling technique before they are hidden. The secret or session keys for scrambling can also
be embedded in the encrypted stego-image. Moreover, they can be encrypted by the asymmetric
cryptographic algorithm, e.g. the RSA (Rivest-Shamir-Adleman) method. It is verified that the
proposed cryptosystem provides a confidential image steganographic method and secret data
hiding scheme to improve transmission security of the secret information.
6. ACKNOWLEDGEMENT
This research was supported by the National Science Council, Taiwan, under the grant of
NSC97-2221-E-468-006.
7. REFERENCES
1. M. Yang, N. Bourbakis, and Li Shujun, “Data-image-video encryption,” IEEE Potentials, vol.
23, no. 3, pp. 28-34, 2004.
2. Y. Govindarajan and S. Dakshinamurthi, “Quality - security uncompromised and plausible
watermarking for patent infringement,” International Journal of Image Processing, vol. 1, no.
2, 2007.
3. T.-S. Chen, C.-C. Chang, and M.-S. Hwang, “A virtual image cryptosystem based on vector
quantization,” IEEE Trans. Image Processing, vol. 7, no. 10, pp. 1485-1488, 1998.
4. Y.-C. Hu, “High-capacity image hiding scheme based on vector quantization,” Pattern
Recognition, vol. 39, no. 9, pp. 1715-1724, 2006.

5. C.-C. Chang, C.-Y. Lin, and Y.-Z. Wang, “New image steganographic methods using run-
length approach,” Information Sciences, vol. 176, no. 22, pp. 3393-3408, 2006.
6. W.-Y. Chen, “Color image steganography scheme using set partitioning in hierarchical trees
coding, digital Fourier transform and adaptive phase modulation,” Applied Mathematics and
Computation, vol. 185, no. 1, pp. 432-448, 2007.
7. P. Refregier and B. Javidi, “Optical image encryption based on input plane and Fourier plane
random encoding,” Optics Letters, vol. 20, pp. 767-769, 1995.
8. G.-S. Lin, H. T. Chang, W.-N. Lie, and C.-H. Chuang, “A public-key-based optical image
cryptosystem based on data embedding techniques,” Optical Engineering, vol. 42, no. 8, pp.
2331-2339, 2003.
9. C.-H. Chuang and G.-S. Lin, “An optical image cryptosystem based on adaptive
steganography,” Optical Engineering, vol. 47, 047002 (9 pages), April 2008.
10. L. Chen and D. Zhao, “Optical color image encryption by wavelength multiplexing and
lensless Fresnel transform holograms,” Optics Express, vol. 14, pp. 8552-8560, 2006.
11. M. Joshi, Chandrashakher, and K. Singh, “Color image encryption and decryption using
fractional Fourier transform,” Optics Communications, vol. 279, pp. 35-42, 2007.
12. Computer Vision Group (CVG), Department of Computer Science and Artificial Intelligence,
University of Granada. Retrieved from http://decsai.ugr.es/cvg/, August 2008.
13. Kodak Lossless True Color Image Suite. Retrieved from http://r0k.us/graphics/kodak/,
August 2008.
14. Programming, Image Processing, and Video Codecs Resourses. Retrieved from
http://www.hlevkin.com/, August 2008.

Data Steganography for Optical Color Image Cryptosystems

More Related Content

What's hot (20)

Viewers also liked (7)

Similar to Data Steganography for Optical Color Image Cryptosystems (20)

Recently uploaded (20)

Data Steganography for Optical Color Image Cryptosystems