A WATERMARKING SYSTEM FOR TEACHING STUDENTS

TO RESPECT INTELLECTUAL PROPERTY RIGHTS

Maria Chroni, Angelos Fylakis and Stavros D. Nikolopoulos

Department of Computer Science, University of Ioannina, GR-45110 Ioannina, Greece

Keywords:

Watermarking Techniques, Intellectual Property Rights, Teaching IP, Educational Tools, Visual Representa-

tions, Learning Process, Color Images, 2D-representations of Permutations, Algorithms.

Abstract:

Our work proposes a watermarking system for supporting the teaching process for educating students to re-

spect intellectual property rights. In particular, we propose an educational tool, which we named WaterIP,

that can be efﬁciently used by students to enable them to perceive how to protect their own ideas and how

to respect others’ intellectual property. Our system uses an efﬁcient technique for watermarking images by

exploiting certain properties of a speciﬁc 2D representation of permutations, it has a friendly graphical user

interface and shows interesting performance ﬁgures. The system provides students with two main working

levels: (I) the student creates a secret key, i.e., the watermark, and embeds it into his own image, and (II) he

makes the marks of a watermarked image visible in order to later extract the watermark from it using only,

for pedagogical reasons, a ruler and a pencil. The watermarking method behind WaterIP can be applied to all

educational levels beginning from early childhood up to preliminary and high schools. We demonstrate the

educational effectiveness of our WaterIP system by presenting ways of how it can be applied in class and show

that WaterIP helps to understand what intellectual property rights really stand for.

1 INTRODUCTION

Digital watermarking (or, simply, watermarking) is a

technique for protecting the intellectual property of a

digital object; the idea is simple: a unique identiﬁer,

which is called watermark, is embedded into a digital

object which may be used to verify its authenticity or

the identity of its owners

The watermarking problem can be described as

the problem of embedding a watermark w into an ob-

ject I and, thus, producing a new object I

, such that

w can be reliably located and extracted from I

even

after I

has been subjected to transformations (Coll-

berg and Nagra, 2010); for example, compression in

case the object is an image.

2 THEORETICAL TOOLS

In this section we present representations of the two

main objects of our watermarking system: the permu-

tations and the digital color images. In particular, we

propose a 2D representation of permutations and give

a 3D representation of color images.

2.1 Permutations

Informally, a permutation of a set of objects S is an

arrangement of those objects into a particular order,

while in a formal (mathematical) way a permutation

of a set of objects S is deﬁned as a bijection from S to

itself (i.e., a map S → S for which every element of S

occurs exactly once as image value).

Permutations may be represented in many ways.

The most straightforward is simply a rearrangement

of the elements of the set S, where S is the set N

{1,2,..., 6}; in this way we think of the permutation

π = (2,5,3,1,6,4) as a rearrangement of the elements

of the set N

such that “1 goes to 2”, “2 goes to 5”, “3

goes to 3”, “4 goes to 1”, and so on. (Sedgewick and

Flajolet, 1996; Golumbic, 1980).

2D Representation of Permutations. In this rep-

resentation, the elements of the permutation π =

(π

,π

,. .. , π

) are mapped in speciﬁc cells of an n×n

matrix A as follows:

• integer i −→ entry A(π

−1

,i)

or, equivalently,

336

Chroni M., Fylakis A. and Nikolopoulos S..

A WATERMARKING SYSTEM FOR TEACHING STUDENTS TO RESPECT INTELLECTUAL PROPERTY RIGHTS.

DOI: 10.5220/0003920103360339

In Proceedings of the 4th International Conference on Computer Supported Education (CSEDU-2012), pages 336-339

ISBN: 978-989-8565-06-8

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

• the cell at row i and column π

is labeled by the

number π

, for each i = 1,2, ...,n.

In our 2DM representation, a permutation π over the

set N

= {1, 2,.. .,n} is represented by an n×n matrix

A as follows:

• the cell at row i and column π

is marked by a

speciﬁc symbol, for each i = 1,2, ...,n.

3 4

Figure 1: A 2DM representation of π = (2,5,3,1,6, 4).

We next present an algorithm which extracts the

permutation π from its 2DM representation matrix.

More precisely, let π be a permutation over N

and let

A be the 2DM representation matrix of π ; given the

matrix A, we can easily extract π from A in linear time

(in the size of matrix A) by the following algorithm:

Algorithm Extract π from 2DM

Input: the 2DM representation matrix A of π;

Output: the permutation π;

1. For each column i of matrix A, 1 ≤ i ≤ n, do:

ﬁnd the marked cell j in the column i,

and set i in cell A( j,i) and 0 in all other cells;

2. For each row i of matrix A, 1 ≤ i ≤ n, do:

ﬁnd the cell j in the row i with value not

equal to 0, and set π

← A(i, j);

3. Return the permutation π;

2.2 Color Images

A digital image is a numeric representation of a 2-

dimensional image; it has a ﬁnite set of values, called

picture elements or pixels, that represent the bright-

ness of a given color at any speciﬁc point in the image.

(Gonzalez and Woods, 2007).

The range of colors can be represented on the

Cartesian 3-dimensional system. The axes x, y and z

are used for the red green and blue color respectively.

In our system, since a color is a triple of integers

(x,y,z), a digital image I of resolution N × M (i.e., it

contains N rows and M columns of pixels) is stored in

a three-dimensional matrix Img of size N × M × 3 as

follows:

if the pixel I(i, j) of the image I has (x,y,z)

color, then Img(i, j,1) = x, Img(i, j, 2) = y, and

Img(i, j,3) = z.

3 OUR WATERMARKING

SYSTEM

In this section we describe the modules and the main

functions of the proposed watermarking system. Our

system, which we named WaterIP, provides to a stu-

dent two main working levels:

• Embed Level. Through a friendly graphical user

interface, the student creates a secret key (i.e., the

watermark w) and selects a picture I in which he

wants to embed the watermark; in our system the

watermark w is a permutation π over the set N

and it is embedded into the original picture I, us-

ing the 2DM representation, resulting the water-

marked picture I

• Mark Level. The student, in order to prove that

he is the owner of the picture I

, inputs the wa-

termarked picture I

into the system which makes

the marks visible to the student so that he will be

able to easily extract the watermark w (i.e., his

secret key) just by looking at the marks; in partic-

ular, the system returns the marked picture I

the student.

We should mention that our system uses a permu-

tation π over the set N

for the watermark w. The set

was selected in purpose; we preferredto use a ﬁxed

size rather than giving the size as a choice to the user.

That choice was made in order to make the system

simple as it is designed to serve educational needs.

Also 6 is not a great number so it is relatively easy to

memorize by a student, nor it is a very small number

and that makes the permutation more presentable.

3.1 Embed Watermark into Image

We next describe an algorithm which embeds a per-

mutation π into an image I;

The algorithm takes as input a permutation π and

an image I, in which the user wants to embed the wa-

termark w = π, and produces the watermarked image

; it works as follows:

Step 1. The algorithm ﬁrst computes the 2DM repre-

sentation of the permutation π = (π

,π

,. ..,π

), that

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337

is, it computes the 6× 6 array A (see, Subsection 2.1);

the entry (i,π

) of the array A contains the symbol “*”,

1 ≤ i ≤ 6.

Step 2. Next, the algorithm takes the input image I

and places on it an imaginary 6× 6 grid which covers

the whole images. Then it scans the image and goes

to each grid-cell C

(I) (there are always 36 grid-cells

in any image) and locates the central pixel p

of the

grid-cell C

(I), 1 ≤ i, j ≤ 6.

Then, it computes the difference between the

brightness of the central pixel p

and the average

brightness of the eight pixels p

, p

,. .., p

around

it, and stores this value in the variable dif(p

)

Finally, it computes the maximum absolute value

of all 36 differences dif(p

), 1 ≤ i, j ≤ 6, and stores it

in the variable Maxdif(I).

Step 3. The algorithm goes again to each central pixel

of each grid-cellC

and if the corresponding entry

A(i, j) contains the symbol “*”, then it increases its

brightness k

by the value e

so that it surpasses the

image’s maximum difference Maxdif(I) by a constant

c; that is, k

+ e

= Maxdif(I) + c.

In our system we use c = 9, and thus the bright-

ness k

of the central pixel of each grid-cell C

increased by e

, where

= Maxdif(I) − k

+ 9 (1)

1 ≤ i, j ≤ 6.

Let I

be the resulting image after increasing the

brightness of the 6 central pixels, with respect to π,

of the image I. Hereafter, we call these 6 central pix-

els as 2DM-pixels; recall that, p

is a 2DM-pixel if

A(i,π

) = “*”, or, equivalently, the cell (i, π

) of the

matrix A is marked.

3.2 Show Marks on Image

Next we describe our system’s algorithm which is re-

sponsible for making the marks of a watermarked im-

age I

visible to the student so that he will be able to

extract, in a speciﬁc way, the watermark w by hand.

The algorithm takes as input a watermarked image

in which the student wants to make the markings

visible, and produces the marked image I

; the steps

of the algorithm are the following:

Step 1. The algorithm places again the same imag-

inary 6 × 6 grid on image I

and locates the central

pixel p

(i.e., the 2DM-pixel) of each grid-cellC

(I),

1 ≤ i, j ≤ 6; there are 36 central pixels in total. Then,

it ﬁnds the 6 central pixels p

, p

,. .., p

, among the

36, with the maximum brightness using a known sort-

ing algorithm.

Figure 2: Part of the WaterIP interface.

Step 2. In this step, the algorithm takes the 6 grid-cell

,. ..,C

of the image I

which correspond to 6

central pixels p

, p

,. .., p

, and places a red ﬁlm over

the whole area of each grid-cell C

, 1 ≤ i ≤ 6.

To this end, the algorithm takes each pixel p

i, j

each grid-cell C

, 1 ≤ i ≤ 6, and modiﬁes the bright-

ness k

i, j

= (x

i, j

) of the pixel p

i, j

as follows:

′

i, j

= (255,y

i, j

) (2)

where, j takes N/6 × M/6 different values. The re-

sulting image is the marked image I

Step 3. Finally, the algorithm returns the marked im-

age I

4 INSIDE THE CLASSROOM

Our work proposes a supporting educational tool for

teaching, inside the classroom, the value of intellec-

tual property. As mentioned before we believe that

the best way to gain people’s respect towards intel-

lectual property rights is to start from the roots, and

that is the early education. Students should be taught,

as part of the ethical education, to respect intellectual

property at schools. (Watkins et al., 2007).

We consider that our tool can be applied in all ed-

ucational levels, as part of various lesson plans, be-

ginning from early childhood; uploading an image

or photo the teacher can demonstrate the system to

preschool learners and set them ethical dilemmas con-

cerning the proper use of images or photos. Teachers

of preliminary and high schools can go on more com-

plex meanings and explain learners the concepts of

watermarking, embedding, extracting, permutation,

image analysis, etc.

We next use an imaginary scenario where two

classmates claim the ownership of the same image,

Alex who is really the owner of the image and Bob

who claims that he is the owner, and show how our

WaterIP system helps both to understand what intel-

lectual property rights really stand for.Then we rec-

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338

ommend a lesson plan of how our WaterIP system

can be efﬁciently applied in groups inside a classroom

during a course.

4.1 Two Classmates

Let us ﬁrst present the scenario of the two classmates,

Alex and Bob, where both claim the ownership of an

image.

The main idea of the scenario is, that Alex ﬁrst

paints a picture at a computer or just takes a photo

and uploads it to the computer. Afterwards he runs

our system and places the numbers between 1 and 6

in a random order that he memorizes and keeps secret.

That order actually forms the permutation which also

is the watermark that Alex wants to embed in his dig-

ital image. He selects from a menu which image he

wants to use and then runs the algorithm and places

the watermark into it. Then he gets the watermarked

image. That watermark will later be his proof that the

picture was really made by him and that he deserves

to be rewarded for it and not someone else; of course

we shall not forget that in order to do that he should

have memorized the permutation in order to claim the

property of the image. Afterwards Alex uploads the

watermarked image in his personal student web-page

or in the lesson’s webpage, making it public.

In Figure 2 we show a form, part of the system’s

friendly user interface, in which Alex produces the

watermark that he wants to embed in his image. What

he does here is that he selects the numbers from the

upper set by clicking on them and places them lin-

early in the second set of numbers which forms the

watermark (Button 1) and then selects and views an

image (Button 2); the ﬁgure depicts an intermediate

stage where Alex has already load an image and has

selected the ﬁst two numbers, i.e., 2 and 5, of his wa-

termark. After the creation of his watermark, he is

able to embed it in the image (Button 3) and save the

watermarked image (Button 4).

Then Bob may later download the picture and in-

sist to the teacher that he painted it. Now the teacher

should pretend that he is going to reward that student

for that picture but also asks him if he can prove that it

is really his image. Of course Alex that really painted

that image would not want another one to be awarded

with a grade for a picture that he painted. So Alex

also gets into the discussion by telling his teacher that

there is a watermark in that picture and also which is

the value of this watermark. This way he can prove

that it is his property whereas Bob won’t be able to

do that. Then Alex uses the WaterIP program again,

inserts the picture that is being claimed by both sides

and gets the marked picture. Last Alex applies the

Figure 3: The marked image I

extracting algorithm, gets printed the marked image,

and using a ruler for his help gets the permutation.

That proves to the teacher that the picture was indeed

painted by Alex as the watermark he was told pre-

viously matches with the one that was just extracted

from the picture. Last the teacher rewards Alex for

his picture.

Figure 3 shows the most important part of the

whole process. After Alex runs the marking algorithm

he has a picture with red ﬁlms over the marked areas

and, using a ruler and a pencil, he follows the extract-

ing procedure:

Procedure Water Extract

1. Scan the picture with the ruler from left to right

and write the numbers from 1 to 6 in ascending

order inside the red ﬁlms;

2. Get the watermark by scanning the picture from

top to bottom and read every number that comes

across;

It is worth mentioning that after the completion of

the above process a student has experienced, he can

then easier understand what intellectual property re-

ally stands for.

REFERENCES

Collberg, C. and Nagra, J. (2010). Surreptitious Software.

Addison-Wesley.

Golumbic, M. (1980). Algorithmic Graph Theory and Per-

fect Graphs. Academic Press, Inc., New York.

Gonzalez, R. C. and Woods, R. E. (2007). Digital Image

Processing. Prentice-Hall, 3rd edition.

Sedgewick, R. and Flajolet, P. (1996). An Introduction to

the Analysis of Algorithms. Addison-Wesley.

Watkins, C., Carnell, E., and Lodge, C. (2007). Effective

Learning in Classrooms. Sage Publications.

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