15-10-2012, 03:44 PM
A Secure Skin Tone based Steganography Using Wavelet Transform
A Secure Skin Tone.docx (Size: 242.61 KB / Downloads: 31)
Abstract
Steganography is the art of hiding the existence of
data in another transmission medium to achieve secret
communication. Steganography method used in this paper is
based on biometrics. And the biometric feature used to
implement Steganography is skin tone region of images [1].
Here secret data is embedded within skin region of image that
will provide an excellent secure location for data hiding. For
this skin tone detection is performed using HSV (Hue,
Saturation and Value) color space. Additionally secret data
embedding is performed using frequency domain approach -
DWT (Discrete Wavelet Transform), DWT outperforms than
DCT (Discrete Cosine Transform). Secret data is hidden in one
of the high frequency sub-band of DWT by tracing skin pixels in
that sub-band. For data hiding two cases are considered, first is
with cropping and other is without cropping. In both the cases
different steps of data hiding are applied either by cropping an
image interactively or without cropping i.e. on whole image.
Both cases are compared and analyzed from different aspects.
This is concluded that both cases offer enough security. Main
feature of with cropping case is that this results into an
enhanced security because cropped region works as a key at
decoding side. Where as without cropping case uses embedding
algorithm that preserves histogram of DWT coefficient after
data embedding also by preventing histogram based attacks
and leading to a more security. This study shows that by
adopting an object oriented steganography mechanism, in the
sense that, we track skin tone objects in image, we get a higher
security. And simulation result shows that satisfactory PSNR
(Peak-Signal-to-Noise Ratio) is also obtained.
INTRODUCTION
In this highly digitalized world, the Internet serves as an
important role for data transmission and sharing. However,
since it is a worldwide and publicized medium, some
confidential data might be stolen, copied, modified, or
destroyed by an unintended observer. Therefore, security
problems become an essential issue. Encryption is a
well-know procedure for secured data transmission [2].
Although encryption achieves certain security effects, they
make the secret messages unreadable and unnatural.
Steganography and Watermarking
Steganography and Watermarking both are branches of
data hiding but they are used for different purposes.
Watermarking is very similar to Steganography in a number
of respects. Both seek to embed information inside a cover
message with little to no degradation of the cover-object.
Digital watermarking is the technique of embedding digital
marks inside a container so that there is a logical way of
extracting the data embedded, while not harming the
container in any perceived way. Steganography uses cover
files to deliver its messages. On the other hand watermarking
considers the cover file as the important data that is to be
preserved. In Steganography purpose of embedded data is to
deliver secret communication. In watermarking, purpose of
embedded data is to supply some additional information
about the cover image such as image owner to verify image’s
ownership to achieve control over the copy process of digital
data. In Steganography, the object of communication is the
hidden message. In digital water-marks, the object of
communication is the cover. In short, Steganography pay
attention to the degree of invisibility while Watermarking
pay most of its attribute to the robustness of the message and
its ability to withstand attacks of removal, such as image
operations(rotation, cropping, filtering) [4].
PROPOSED METHOD
Proposed method introduces a new method of embedding
secret data within skin region as it is not that much sensitive
to HVS (Human Visual System) [1].This takes advantage of
biometrics features such as skin tone, instead of embedding
data anywhere in image, data will be embedded in selected
regions. Overview of method is briefly introduced as follows.
At first skin tone detection is performed on input image using
HSV (Hue, saturation, value) color space. Secondly, cover
image is transformed in frequency domain. This is performed
by applying
Haar-DWT, the simplest DWT on image leading to four
sub-bands. Then payload (number of bits in which we can
hide data) is calculated. Finally, secret data embedding is
performed in one of the high frequency sub-band by tracing
skin pixels in that band. All these embedding steps are
applied to two cases: a] With Cropping b] Without Cropping.
Comparison and analysis of both cases is done. For the with
cropping case before performing all above mentioned
embedding steps cropping on input image is performed and
then in only cropped region data hiding is performed, not in
whole image. Cropped region works as a key at decoding side
so cropping results into more security. Both cases uses
different embedding algorithm. In without cropping case,
embedding algorithm attempts to preserve histogram of
DWT coefficients after embedding also. This protects from
histogram based first order statistics attacks. Ultimately it is
observed that both the cases provide enough security. In both
cases embedding process affects only certain Regions of
Interest (ROI) rather than the entire image. So utilizing
objects within images can be more advantageous. This is also
called as Object Oriented steganography [1]. Next
sub-sections describe encoding, decoding process in detail
and briefly introduce skin tone detection and DWT.
Performance of the proposed method
For with cropping case, after embedding secret data in
cropped image, resulted cropped stego image is shown in Fig.
9(a). (Result of step 5 of encoding process of with cropping
case). As this doesn’t look like cover image merging is
performed to obtain final stego image that is shown in Fig.
9(b). (Result of step 6 of encoding process of with cropping
case). For merging co-ordinates of first and last pixels of
cropped image in original image are calculated. After
performing decoding process on stego image obtained in
with cropping case, retrieved image is shown inFig.
9©. Same proposed method is implemented for without
cropping case. In without cropping case secret data is hidden
in one of the sub-band which is obtained by performing the
DWT on whole image and not only to cropped region and by
applying new embedding algorithm described earlier for
preservation of histogram. Resultant stego image obtained in
without cropping case is shown in Fig. 10(a). It is observed
that this preserves histogram of DWT coefficients after
embedding also. After performing decoding on this stego
image, retrieved secret image is shown in Fig. 10(b). PSNR is
calculated for four different final stego images resulted from
a considered image and three more sample images, here
secret image used for embedding is shown in Fig. 8. PSNR
for with and without cropping case is shown in table 1.
Average PSNR of proposed method is calculated based on
the obtained PSNR.
CONCLUSION
Digital Steganography is a fascinating scientific area
which falls under the umbrella of security systems. In this
paper Biometric Steganography is presented that uses skin
region of images in DWT domain for embedding secret data.
By embedding data in only certain region (here skin region)
and not in whole image security is enhanced. Two cases for
data embedding are considered, with cropping and without
cropping. It is ob served that both offer enough security. In
with cropping case no one can extract message without
having value of cropped region. Where as without cropping
case uses embedding algorithm that preserves histogram of
DWT coefficients after embedding also and prevents
histogram based attacks. According to simulation results,
proposed approach provides fine image quality.