Ant Colony Optimization (ACO) based Data Hiding in Image Complex Region

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 8, No. 1, February 2018, pp. 379~389
ISSN: 2088-8708, DOI: 10.11591/ijece.v8i1.pp379-389  379
Journal homepage: http://iaescore.com/journals/index.php/IJECE
Ant Colony Optimization (ACO) based Data Hiding in Image
Complex Region
Sahib Khan
Department of Electronics and Telecommunications, Politecnico Di Torino, 10129 Italy
Article Info ABSTRACT
Article history:
Received Sep 9, 2017
Revised Dec 25, 2017
Accepted Jan 11, 2018
This paper presents data an Ant colony optimization (ACO) based data
hiding technique. ACO is used to detect complex region of cover image and
afterward, least significant bits (LSB) substitution is used to hide secret
information in the detected complex regions’ pixels. ACO is an algorithm
developed inspired by the inborn manners of ant species. The ant leaves
pheromone on the ground for searching food and provisions. The proposed
ACO-based data hiding in complex region establishes an array of
pheromone, also called pheromone matrix, which represents the complex
region in sequence at each pixel position of the cover image. The pheromone
matrix is developed according to the movements of ants, determined by local
differences of the image element’s intensity. The least significant bits of
complex region pixels are substituted with message bits, to hide secret
information. The experimental results, provided, show the significance of the
performance of the proposed method.
Keyword:
Ant colony optimization
Edge detection
LSB Steganography
Pheromone matrix
Steganalysis
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Sahib Khan,
Department of Electronics and Telecommunications,
Politecnico di Torino,
Corso Duca degli Abruzzi, 24, 10129 Torino TO, Italy.
Email: sahib.khan@polito.it, engrsahib_khn@yahoo.com
1. INTRODUCTION
Image steganography is information hiding technique that use digital image as cover media. Along
with secret exchange of information, it has various other applications e.g. copyright, data integrity and
authentication [1], [2]. Digital audio, video and text can also be used as a cover, but image is adopted most
widely for this purpose due its high redundancy.
Data hiding techniques are explored by many researchers and proposed various good hiding
techniques to insure security of hidden information. Honsinger et al.’s and Fridrich et al.’s proposed
steganography methods in spatial domain by hiding secret information directly in image pixels [3], [4]. Sahib
et al. proposed variable least significant bits (VLSB) steganography and presented techniques, like modular
distance technique (MDT) [5], decreasing distance decreasing bits algorithm (DDDBA) [6], varying index
varying bits substitution (VIVBS) algorithm [7]. Sahib et al., inspired from chipper block chaining (CBC)
encryption, proposed new techniques of stego block chaining (SBC) and enhanced stego block chaining
(ESBC) to hide information in digital images [8].
The aim of all data hiding techniques is to make the presence of hidden information undetectable
and this attracted the attention of researcher to make use of HVS limitation. HVS can very easily detect the
variations made in smooth area of cover image as compared to the changes in complex region. Due to this
characteristic of the HVS, complex region of cover image is subjected to hiding and smooth region is not
modified [9], [10]. In some techniques, complex region is subjected to more to data hiding than smooth
region. This approach results in high quality of the stego-image, which means increase in the security of
hidden information. Various techniques, including LSB methods [11], PVD methods, and side-match

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 8, No. 1, February 2018 : 379 – 389
380
methods [12], [13], have been proposed to hide information in complex area of cover; a detail can be found
in [14], [15]. But, these techniques present a low hiding capacity and don’t comply completely with the rules
that the complex region can bear more changes than smooth region [12], [13]. To increase data hiding
capacity Jung et al. [16] presented a new technique that hides data in smooth areas along with edges, but
results in more distortion. However, the methods adopted by these data hiding techniques for detection of
complex region are more vulnerable to noise.
The proposed technique is one such effort towards data hiding in complex region of cover image.
The hiding information in cover media does not attract the human attention and the presence of hidden
information is not perceivable to HVS. This technique made use of ACO, a nature-inspired optimization
algorithm [17-19] for detection of complex region [20], and secret information are embedded in LSB of the
complex region pixels [21], [22]. The forthcoming contents of the paper are organized as follow. Section 2,
presents the ACO based data hiding in complex, the experimental results are presented in Section 3 and at
end papers is concluded with Section 4.
2. PROPOSED TECHNIQUE
The detection of complex region in cover image is the key step in hiding of information in complex
region. There various methods to detect complex region in images. These methods include canny edge
detection, deriche, differential, sobel, prewitt, Roberts cross and other methods. These methods are very
efficient to detect complex region in digital images but, these methods don’t comply completely with the
rules that the complex region and hide data in complex region previous methods hide data in the complex
region of cover image as most of these methods detect weak and disconnected edge pixels and consider that
as true complex region. But, these techniques also hide data in those pixels that doesn’t belong to edges and
are more vulnerable to noise. In this paper an ACO based technique has been used to detect complex region
in cover image [23], [24] and then to target this region for data hiding using LSB steganography.
ACO-based image edge detection approach, construct a pheromone matrix, utilizing many ants to
move on a 2-D image. The movement of the ants is guided by the local differences of the image pixel’s
intensity values. The entries of the pheromone matrix represent the edge information at each pixel location of
the cover image. The ACO based technique is initialized first and run for N iterations to build pheromone
matrix. The process performs both construction and update steps iteratively. At the end decision process is
used to determine the pixels belong to complex region. The whole process is explained here
in detail as follow.
2.1. Initialization
A digital image is an array of pixels with intensity level I. Let consider a grayscale image of
size , as cover medium. A total of ants are randomly assigned on an image . Each pixel of the
cover image is considered as a node. To initialize the complex region detection, process the initial value of
each pheromone matrix’s component is set to a constant .
2.2. Construction
The construction process is composed various steps, at the nth construction-step, one ant, from a
total of ant, is randomly selected. The selected ant can move over the cover image for movement
steps. The movement of the ant from initial node to its neighbor node is done according to the
transition probability as given by Equation (1)
( )
∑ ( )
(1)
Where
Pheromone value at node
Neighborhood (4 or 8-connected) node of the node
Heuristic information at node
: Influence of pheromone matrix
: Influence of heuristic matrix
The heuristic information at any node is calculated using Equation (2).

Int J Elec & Comp Eng ISSN: 2088-8708 
Ant Colony Optimization (ACO) based Data Hiding in... (Sahib Khan)
381
(2)
Where is the normalization factor and given by Equation (3).
∑ ∑ (3)
Where
The intensity level pixel of image C
The depends on the variation in gray levels of strength of pixels in the clique is
represented as by Equation (4)
( ) (| | | | | | | |
| | | | | | | |) (4)
To calculate f (.) there are four different function Flat, Gaussian, Sine and Wave and each of them is
considered in this paper and are given here in Equation (5) to Equation (8).
(5)
(6)
{
( )
(7)
{
( )
(8)
Where
: The shape control parameter for functions.
2.3. Updating Stage
The pheromone matrix is updated in two steps. The first updating is performed, in each construction
step, after the movement of each ant, according to Equation (9)
{ (9)
Where
The evaporation rates
: Determined by heuristic matrix is equal to
When the entire ant completes their movement in each construction step, the second updating
process is performed using Equation (10).
(10)
Where
The pheromone decay coefficient
2.4. Decision Stage
The decision process is the final is binary decision-making process to decide whether the pixel
belong to complex region or smooth region. In this a threshold is applied on the final pheromone matrix .
The threshold is computed according to the technique presented in [20].
The mean of value of pheromone matrix is selected as initial threshold . Then all the pheromone
matrix entries are divided in two groups. One group contains all the value smaller than the initial threshold
and other possess the value greater than the initail threshold . Means values of each of the group is

 ISSN: 2088-8708
382
calculated and new threshold is defined as the average of both means. The process of for calculation of
threshold is repeated till the threshold value reaches to a stable value in term of user define tolerance .
Final decision for each pixel at is made on the bases of the pheromone vale at postion
compared with the final threshold value as given by Equation (11).
{ (11)
Where
: The baniary image
If the pheromone value at current position is greater than threshold it is, consider as a part of
complex region and other its treated as smooth region pixel.
2.5. Sub Section 1
The data hiding step is the LSB substitution process. This stage hides secret information in the LSBs
of the cover image on the bases of the complex region detected. In this process whole cover image is
considered and is processed pixel by pixel. Each pixel is check whether it belongs to complex region or
smooth region. If the pixel corresponds to smooth region it is left unaffected and another pixel is considered.
And if the pixel belongs to complex region then its LSB bits are substitued with the secret information.
This process continues until the whole cover image is explored. The hiding process is accomplished in
following manner.
A pixel is cosider as complex region pixel if its correspding =0 and is consider smooth
region componant if . Lets cosider a secret messahe to be hidden in complex region and is final
stego image obtained after information hiding. The stego image is given by Equation (12).
{ (12)
Figure 1. ACO-based data hiding in complex region
3. IMPLEMENTATION, EXPERIMENTAL RESULTS AND ANALYSIS
To hide secret data in complex region of cover image using ACO algorithm and get experimental
results, many different cover images are used. These cover images include, Cameraman, Lena, House, Jelly

383
beans, Mandrill, Pepper, Tiffany and Tree as presented in Figures 2(a), 2(b), 2(c), 2(d), 2(e), 2(f), 2(g) and
2(h), respectively. All these cover images are taken from image used are of the same size of . Each
of the cover is subjected to data hiding using the proposed technique. As discusses earlier ACO can be used
for complex region detection using four different functions i.e. Flat, Gaussian, Sine and Wave as given by
Equation (5) to Equation (8), respectively. After the complex region and smooth region’s pixel classification,
an LSB substitution technique is used for data hiding in the complex region’s pixels only.
ACO approach is dependent on a very large number of parameters. The parameters set for the
experimentation are given as:
The shape control parameter λ= 10
The influence of pheromone matrix α = 1
The influence of heuristic matrix = 0.1
The evaporation rate = 0.1
The pheromone decay coefficient = 0.05
To analyze the proposed technique quantitatively, the data hiding capacity the MSE and PSNR are
calculated as given by Equation (13) to Equation (15), respectively [20].
(13)
∑ ∑
(14)
(15)
(a) (b) (c)
(d) (e) (f)
(g) (h)
Figure 2. Cover Images (a) Cameraman, (b) Lena, (c) House, (d) Jelly Beans, (e) Mandrill, (f)
Pepper, (g) Tiffany, and (h) Tree

 ISSN: 2088-8708
384
Firstly, the proposed technique is applied on all the cover images shown in Figure 2. Flat function as
given by Equation (5) has been used in ACO based complex region detection. The stego images obtained are
shown in Figure (3). The hiding capacity, MSE and PSNR calculated for each cover image is listed
in Table 1.
(a) (b) (c)
(d) (e) (f)
(g) (h)
Figure 3. Stego Images obtained using Flat function given in Equation (5) (a) Cameraman, (b) Lena, (c)
House, (d) Jelly Beans, (e) Mandrill, (f) Pepper, (g) Tiffany, and (h) Tree
Table 1. Hiding Capacity, PSNR and MSE using Flat Function
Cover Image PSNR (dB) MSE Hiding Capacity (%)
Cameraman 45.74131 1.7336 4.1229
Lena 50.27679 0.6101 4.5105
House 46.43288 1.4784 4.0344
Jelly Beans 46.71787 1.3845 4.0558
Mandrill 45.95596 1.6500 5.5817
Pepper 47.14022 1.2562 4.5776
Tiffany 45.84321 1.6934 4.5837
Tree 45.77451 1.7204 5.2399
Secondly, ACO based data hiding in complex region techniques is implemented the same cover
images shown in Figure 2, but using Gaussian function, as given by Equation (6). The resulted stego images

385
are displayed here Figure 4. The hiding capacity, MSE and PSNR calculated, using each cover image for
information hiding, is listed in Table 2.
(a) (b) (c)
(d) (e) (f)
(g) (h)
Figure 4. Stego Images obtained using Gaussian function given in Equation (6) (a) Cameraman, (b) Lena, (c)
Table 2. Hiding Capacity, PSNR and MSE using GaussianFunction
Cameraman 46.23899 1.5459 3.1647
Lena 52.68888 0.3501 2.1667
House 48.79289 0.8586 1.8951
Jelly Beans 48.3673 0.9470 2.4780
Mandrill 50.35944 0.5986 2.6489
Pepper 48.23987 0.9752 2.9724
Tiffany 49.16224 0.7886 2.0294
Tree 47.12503 1.2606 4.3274
Similarly, in third step all the cover images given in Figure 2 are subjected to data hiding using the
proposed technique. Moreover, this time Sine function given in Equation (7) is used in ACO complex region
detection. The obtained stego images, with hidden information inside it, are shown here Figure (5). The
Table 3 contains the calculated hiding capacity, MSE and PSNR for all cover images.

 ISSN: 2088-8708
386
(a) (b) (c)
(d) (e) (f)
(g) (h)
Figure 5. Stego Images obtained using Sine function given in Equation (7) (a) Cameraman, (b) Lena, (c)
Table 3. Hiding Capacity, PSNR and MSE using Sine Function
Cameraman 45.23581 1.9476 4.4312
Lena 50.54471 0.5736 4.3854
House 46.63738 1.4104 3.7842
Jelly Beans 45.67469 1.7604 4.1870
Mandrill 46.5508 1.4388 5.8777
Pepper 46.12038 1.5887 4.5044
Tiffany 46.13627 1.5829 4.4250
Tree 45.81178 1.7057 5.5786
Lastly, Wave function, mathematically given by Equation (8), is used by ACO algorithm to classify,
the complex and smooth region’s pixels and the cover images obtained after data hiding in complex region
are shown in Figure 6. The values of hiding capacity, MSE and PSNR are listed in Table 4.
The results show that all the ACO based data hiding in complex region results in significantly high
quality stego images. However, Flat function in Equation (5) and Sine function in Equation (7), are very
efficient both in term of hiding capacity and stego image quality.

387
(a) (b) (c)
(d) (e) (f)
(g) (h)
Figure 6. Stego Images obtained using Wave function given in Equation (8) (a) Cameraman, (b) Lena, (c)
Table 4. Hiding Capacity, PSNR and MSE using Wave Function
Cameraman 46.90996 1.3246 2.9755
Lena 52.65306 0.3530 2.4200
House 48.5624 0.9054 2.0874
Jelly Beans 47.54692 1.1439 2.8076
Mandrill 48.88336 0.8409 2.8473
Pepper 47.8757 1.0605 2.8661
Tiffany 49.45377 0.7374 2.1210
Tree 46.90079 1.3274 4.1229
4. COMPARISON WITH OTHER TECHNIQUES
As discussed in Section 3, the proposed method results in a very high quality stego images with
PSNR greater than 100dB for all images using all four functions mentioned in Section 2. Here, this represents
the comparison of the proposed method with different previous techniques. As the proposed technique is data
hiding method that hides secret information in complex region of cover images. Therefore, a comparison of
the proposed technique is made only with the data hiding techniques that uses edges or complex region of
cover images. The comparison is made with Fridrich et al. [3], Honsinger et al. [4], Khan et al. [24], Goljan

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388
et al. [25], Macq and Dewey [26], and Vleeschouwer et al. [27], in term of PSNR and hiding capacity. The
comparison made using Lena and Mandrill as cover images. The resulted values of hiding capacity and
PNSR are listed in Table 5.
The Honsinger et al. and Fridrich et al. techniques results in a hiding capacity of less than 0.0156
bpp and 0.0156 bpp, respectively. Vleeschourwer et al. achieve a hiding capacity of 0.156 bpp, with
significantly stego image quality of 30dB in term of PSNR. Macq and Deweyand proposed technique,
increases the data hiding capacity and results in a hiding capacity more than Honsinger et al., Fridrich et al.
and Vleeschourwer et al. techniques. A hiding capacity of less than 0.03125 is recorded, but the visual
quality of the stego image is affected very much. Goljan et al. and Khan et al. presented techniques claim
hiding capacity of 0.36bpp and 0.33bpp, with PSNR of 39.00dB and 46.23dB respectively. The proposed
technique resulted in a hiding capacity of 0.36 and 0.44 with PNSR greater than 50dB and 45dB for Lena and
Mandrill cover images, correspondingly.
The Table 5 shows that the PSNR of the proposed technique is significantly higher than the PSNR
values of all the other techniques. While, the hiding capacity of the proposed technique is also higher than all
techniques except that of Goljan et al. and Khan et al. techniques. ACO based data hiding in complex region
has equal data hiding capacity as that of Goljan et al. but, the quality of the stego images is significantly
better than the Goljan et al.
Table 5. Comparison of proposed method with other methods
Technique
Lena Lena
Hiding Capacity (bpp) PSNR (dB) Hiding Capacity (bpp) PSNR (dB)
Honsinger et al. <0.0156 - <0.0156 -
Fridrich et al. 0.0156 - 0.0156 -
Vleeschouwer et al. 0.0156 30.00 0.0156 29.00
Macq and Deweyand <0.03125 - <0.03125 -
Goljan et al. 0.36 39.00 0.44 39.00
Khan et al. 0.33 46.23 0.669 44.12
Porposed technique 0.36 >50 0.44 >45
5. CONCLUSION
ACO based data hiding in complex region of digital images is an efficient data hiding technique that
successfully exploits the HVS limitation of less sensitivity to the changes in complex regions. This technique
results in high data hiding capacity with significantly good quality stego image. The beauty of the proposed
technique lies in the fact that hiding capacity can be controlled by changing the function in ACO
classification stage and the hiding capacity can be increased significantly with affecting the PSNR by
choosing either Flat function in Equation (5) or Sine function in Equation (7), instead of Gaussian and Wave
functions in Equation (6) and Equation (8), respectively. The hiding capacity and PSNR of the proposed work
is higher than or comparable to other methods. The PSNR of the proposed method remain above 100 dB for
all images as discussed in Section IV. In short, the ACO based data hiding in complex region technique is an
efficient and secure data hiding method, resulting in a high quality stego-image, significantly high PSNR and
reasonable data hiding capacity.
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