Print-Scan Resilient Image Steganography
Adriell Dagasuan, student, ADMU, and Adrin Del Rosario, student, ADMU

Abstract—This thesis research aims to develop a way of
performing a print-scan resilient image steganography with blind
decoding . Data would be hidden on an image through a C++
based algorithm. The encoding algorithm will manipulate the
image represented in PPMA format. The resulting stega image
will then be printed. For the decoding process, the printed stega
image will be scanned and the data will be extracted using a C++
based algorithm.
Index Terms—steganography, print-scan resilient data hiding,
data hiding using yellow markings
I.
INTRODUCTION
TEGANOGRAPHY is the art and science of hiding
Smessages; a steganographic system thus embeds hidden
data content in unremarkable cover media so as not to arouse
an eavesdropper's suspicion. Steganography has been used for
hundreds of years and in today’s society there are many uses
for steganography. Most modern day uses are in the area of
security. Corporate espionage is one way steganography can
be used. An employee could get a job at a specific company
with the intention of stealing valuable information from them.
One way to pass the information back would be through
steganography.
In today’s society the most practical implementation of
steganography is used in the world of computers. Data is the
heart of computer communication and over the year a lot of
methods have been created to accomplish the goal of using
steganography to hide data. The best object up to this writing
is probably a digital image. Digital images have the benefit of
containing massive amounts of bytes to designate pixel color
for the photo.
II. SIGNIFICANCE OF THE STUDY
This thesis research aims to extend the range of applications
of steganography to printed images by developing an
algorithm that allows a print-scan resilient steganography.
III. SUMMARY OF PREVIOUS RESEARCHES DONE
A. Fujitsu's Implementation
Fujitsu’s technique works by taking advantage of the
sensitivities of the human eye, which struggles to see the
colour yellow. The key is to take the yellow hue in the picture

skew it slightly to create a pattern. A camera is perfectly
sensitive to that yellow hue but the human eye doesn’t see it
very well. The implementation divides the original image into
smaller blocks of 0.8mm square or less. The average gradation
or density level of each block is analyzed and then the
information is added as a sequence of yellow dots which have
a lower gradation. Depending on the bit value, the
implementation would place the yellow dots on the left side or
right side of a cell.
B. Print and Scan Resilient data Hiding in Images
Solanki, et. al, proposed methods to hide information into
images that achieve robustness against printing and scanning
with blind decoding. The selective embedding in low
frequencies scheme hides information in the magnitude of
selected low-frequency discrete Fourier transform coefficients.
The differential quantization index modulation scheme embeds
information in the phase spectrum of images by quantizing the
difference in phase of adjacent frequency locations. A
significant contribution of their paper is analytical and
experimental modeling of the print-scan process, which forms
the basis of the proposed embedding schemes. A novel
approach for estimating the rotation undergone by the image
during the scanning process is also proposed, which
specifically exploits the knowledge of the digital halftoning
scheme employed by the printer. Using the proposed methods,
several hundred information bits can be embedded into images
with perfect recovery against the print-scan operation.
IV. SIGNIFICANT RESULTS
A. Images undergoing the printing and scanning process
We tried to see how much an image differs once it has been
printed and scanned (print-scan image). We used The GIMP to
obtain the PPMA format of the images. We tried comparing
various images from its print-scan counterpart and the results
were all the same. The image changes significantly after it has
undergone the printing and scanning process. We took this
heavily into our consideration in planning for our encoding
and decoding algorithm.
B. Using pure yellow pixels
We tried to implement the steganography implementation
used by Fujitsu. To hide data, we tried replacing some pixels
with pure hues of yellow (R=255, G=255, B=0).
Our algorithm asks for a pattern. This pattern determines
which pixels in an image cell would be replaced by yellow
pixels. Although we could specify a pattern similar to
Fujitsu's,we specified other patterns that could probably reduce
the size of the cell, thus increasing the data capacity of an
image.
Once the pattern, the name of the cover image, and the data
have been specified, the encoding process will start. This will
replace the pixels, specified by the pattern, with yellow pixels
regardless of the original values of these pixels.
The resulting stega image was very much degraded. It won't
pass as a stega image because the yellow pixels are very
obvious and significantly changed the image. The printed
image is the same. Suspicious-looking yellow dots can be
easily identified. This result prompted us to change our
approach in changing the values of the pixels.
D. Decoding the pre-print-scan stega image
C. Using a factor to bring out the yellow
Since giving the pattern-specified pixels with pure yellow
hues gives away the presence of data, we tried another
approach. This time, we considered the original values of the
pixels; Instead of replacing the pixels with pure yellow hues
(R=255, G=255 ,B=0), we reduced the blue component to a
certain factor. For example, we multiplied the blue component
of the pixel with 0.5. This gives the pixel a yellow tint. The
yellow dots becomes more obvious as the factor is reduced.
Using a factor of 0.1, for example, makes the yellow dots in
the stega image more visible as compared to a stega image
which used a factor of 0.8.
The resulting stega images are satisfactory until about a
factor of 0.5. We digressed from the Fujitsu implementation by
using a single pattern but with different factors to differentiate
the 0-bit from the 1-bit. We arbitrarily assigned a factor of 0.8
to 0-bit and a factor of 0.5 to 1-bit.
For the decoding process, we analyzed a cell to determine
if it is embedded with 0-bit or 1-bit by getting the ratio of the
values of the blue component of pattern-specified pixels
(those whose blue component values were reduced) and
pattern-unspecified pixels (those whose blue component
values were retained). This ratio corresponds to the factor
that was used in the encoding process. By changing certain
parameters in the algorithm, we were able to achieve up to
100% data recovery.
E. Decoding the print-scan stega image
Although the decoding process worked well for the preprint-scan stega image, it did not work out at all for the printscan image. Upon inspection, the ratio between the patternspecified and pattern-unspecified pixels differed greatly from
the factor used during the encoding process. This is due to
the printing and scanning process. Our next step is to
assimilate in our encoding process, an image analysis that
would verify if the data is recoverable from the print-scan
image
REFERENCES
[1]
[2]
[3]
Fujitsu Ltd., “Printed Steganography Embedding Invisible Data in a
Picture Image,”Science Links Japan, 2005.
K. Solanki, U. Madhow, B. S. Manjunath, S. Chandrasekaran, and I.ElKhalil, “Print and Scan Resilient data Hiding in Images,” IEEE Trans .
Information forensics and security, vol. 1, no. 4, December 2006.
M. S. Fu and O. C. Au, “Data hiding watermarking in halftone images,”
IEEE Trans. Image Process., vol. 11, no. 4, April 2002.
Adriell Matthew Julius A. Dagasuan
Adrin V. Del Rosario