AN ALGORITHM FOR AUTHENTICATION

OF DIGITAL IMAGES

Dan Dumitru Burdescu, Liana Stanescu

Faculty of Automation, Computers and Electronics,University of Craiova, Bvd. Decebal, Craiova, Romania

Keywords: Watermarking, Robustness, Multimedia, JPEG, Algorithm.

Abstract: The rapid growth of digital multimedia technologies brings tremendous attention to the field of digital

authentication. The owner or the distributor of the digital images can insert a unique watermark into copies

for different customers or receivers, which will be helpful to identify the source of illegal copies. In digital

watermarking, robustness is still a challenging problem if different sets of attacks need to be tolerated

simultaneously. In this paper we present an original spatial authentication technique for digital images. Our

approach modifies blocks of the image by insertion of a spatial watermark. A spatial mask of suitable size is

used to hide data with less visual impairments. The watermark insertion process exploits average color of

the homogeneity regions of the cover image. The complexity of the algorithms is proved to be O(n

), where

‘n’ is the nodes number of virtual graph for watermark. The authentication method developed below works

for all types of digital image.

1 INTRODUCTION

In recent years, the rapid and extensive growth in

Internet technology is creating a pressing need to

develop several newer techniques to protect

media. This necessity arises because the digital

representation of media possesses inherent

advantages of portability, efficiency and accuracy of

information content. On the other hand, this

representation also implies a serious threat of easy,

accurate and illegal perfect copies in unlimited

numbers. Unfortunately, the currently available

formats for image, (also audio and video) in digital

form do not allow any type of copyright protection.

A potential solution to this kind of problem is an

electronic stamp or digital watermark, which is

intended to complement cryptographic processing.

The later techniques facilitate access of the

encrypted data only for valid key holders but fail to

track any reproduction or retransmission of data

after decryption. On the other hand, in digital

watermarking, an identification code is embedded

permanently inside a cover image, which remains

within that cover invisibly even after decryption

process. Digital watermarking is now considered an

efficient technology for copyright protection.

Several image watermark schemes have been

developed in the past few years, both spatial and

frequency domains being used for watermark

embedding. Spatial watermarks are constructed in

the image spatial domain, and embedded directly

into an image’s pixel data (Burdescu, 2004).

General requirements for watermarking

techniques, in general, need to posses the following

characteristics: (a) imperceptibility for hidden

information, (b) redundancy in distribution of the

hidden information inside the cover image to satisfy

robustness in the watermark extraction process even

from the cropped watermarked image; and (c)

possible use of one or more keys to achieve

cryptographic security of hidden content

(Katzenbesser, 2000, Lee and Jung , 2001).

Besides these general properties, an ideal

watermarking system should also be resilient to

insertion of additional watermarks to retain the

rightful ownership. The perceptually invisible data

hiding requires insertion of the watermark in higher

spatial frequency of the cover image since the

human eye is less sensitive to this range of

frequencies. But in most of the natural images the

majority of visual information is concentrated on the

lower end of the frequency band. Thus the

information hidden in the higher frequencies

components might be lost after quantization

303

Dumitru Burdescu D. and Stanescu L. (2006).

AN ALGORITHM FOR AUTHENTICATION OF DIGITAL IMAGES.

In Proceedings of the International Conference on Security and Cryptography, pages 303-308

DOI: 10.5220/0002099403030308

 SciTePress

operation of losing compression (Hsu, 1999). This

motivates researchers to realize the importance of

perceptual modeling of the human visual system and

the need to embed a signal in perceptually

significant regions of an image, especially if the

watermark is to survive losing compression (Cox,

1997). In the spatial domain block based approach

this perceptually significant region is synonymous of

low variance blocks of the cover image.

Since the meaning of multimedia data is based

on its content, it is necessary to modify the

multimedia bit-stream to embed some codes, i. e.

watermarks, without changing the meaning of the

content. The embedded watermark may represent

either a specific digital producer identification label,

or some content-based codes generated by applying

a specific rule. Because the watermarks are

embedded in the data content, once the data is

manipulated, these watermarks will also be modified

such that the authenticator can examine them to

verify the integrity of the data. For complete

verification of uncompressed raw multimedia data,

watermarking may work better than digital signature

methods because: (a) the watermarks are always

integrated with the data such that the authenticator

can examine them conveniently, and (b) there are

many spaces in the multimedia data to embed the

watermarks without degrading the quality too much

(Yeung, 1997). However, there is no advantage to

use the watermarking method in a compressed

multimedia data for complete verification.

Compression standards e.g. JPEG or MPEG have

user-defined sections where digital signatures can be

placed. Because multimedia data are stored or

distributed in the file format instead of pixel values,

therefore the digital signature can be considered as

being “embedded” in the data. For content

verification, a watermarking method that can

reliably distinguish compression from other

manipulations still has not been found. The

watermarks are either too fragile for compression or

too flexible for manipulation.

So far, the robust watermarking systems found in

the literature can only withstand some of the

possible external attacks but not all. The attacks

against the watermark try to neutralize the

watermark, without damaging the image too much.

The watermark is neutralized if: (a) the detector

cannot detect the watermark (distortion, attenuation

etc.), (b) the detector cannot recognize the

watermark in the image from another one, and (c)

the watermark is no longer in the image. The attacks

can be very different: (a) in the spatial domain, it can

be scaling, cropping, rotation, noise addition. The

open source software STIRMARK available on the

Web generates many of these attacks, (b) in the

frequential domain it can be filtering, (c)

compression and (d) adding another watermark over

the first one. The STIRMARK software generates

random rotations and distortions on blocks of the

image. The STIRMARK software simulates JPEG

coding, filtering operations, rotation, scaling and

cropping. The result is very slight alterations on

image, but watermarks are usually heavily damaged.

The present paper describes a computationally

efficient block-based spatial domain authentication

technique for a one level watermark symbol. The

selection of the required pixels is based on variance

of the block and watermark insertion exploits

average color of the blocks. The proposed

algorithms were tested on a few of the most usual

transformations of images and the obtained results

showed that the proposed method is efficient. The

authentication method developed below works for

all types of digital image and it can be applied in

medical domain because it can be inserted into

images immediately when the image is obtained by a

medical apparatus.

2 AUTHENTICATION

ALGORITHMS

All watermarking methods share the same building

blocks – an embedding system and the watermark

extraction or recovery system (

Hsu et al., 1999). Any

generic embedding system should have as inputs: a

cover data/image (I), a watermark symbol (W) and a

key (k) to enforce security. The output of the

embedding process is always the watermarked

data/image (I’). The generic watermark recovery

process needs the watermarked data (I’), the secret

key (K) and depending on the method, the original

data (I) and/or the original watermark (W) as input

while the output is the recovered watermark with

some kind of confidence measure for the given

watermark symbol or an indication about the

presence of watermark in the cover image under

inspection. The original cover image I is a standard

image of size N

N where N = 2

with a 24 bit RGB

format (in medical domain the image is read in a

.bmp format). In the proposed work a binary image

of size 256

256 or 512

512 is considered. Each

image is marked with a watermark coefficient. That

means, for each pixel, the value of the pixel is

changed according to the given formula:

D(i,j) = C(i,j) + a*M*W (1)

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where C(i,j) is the original value of a pixel at

position (i,j); D(i,j) is the watermarked value of the

same pixel; a is a scalar factor (here a is chosen

constant, but can be a variable of the position to

improve the invisibility of the watermark and its

detection); M is the mean of the block; and W is the

watermark coefficient to be embedded. In our work,

W could take the values +1 or –1 (one can easily

extend the implementation to M).

Figure 1: Block diagram of a watermark embedding

system.

Figure 2: Generic watermark recovery scheme.

The pixels of the image are arranged into a

virtual hexagon. Then the image is viewed as a

graph not as a pixel matrix. The vertices represent

the pixels and the edges represents neighborhood

between pixels. The algorithm for this operation is

as following:

PROCEDURE Construct_Graph(Image I,edge)

BEGIN

for * i->0,width/edge – 3*edge

for * j->0;height/3

if (i modulo 3==0)

then * if (jmodulo 2 ==0)

then

bmap[i][j]=bmp.bmap[edge*i

][edge*j+edge-1];

end if;

* if(jmodulo 2==1)

then

bmap[i][j]=bmp.bmap[edge*i

][edge*j+edge+2];

end if;

if (i modulo 3==1)

then * if (jmodulo 2 ==0)

then

bmap[i][j]=bmp.bmap[edge*i

-1][edge*j-edge];

end if;

* if (jmodulo 2 ==1)

then

bmap[i][j]=bmp.bmap[edge*i

-1][edge*j+edge*2];

end if;

if (i modulo 3==2)

then * if(j modulo 2==0)

then

bmap[i][j]=bmp.bmap[edge*i

-2][edge*j+edge-1];

end if;

* if(j modulo 2==1)

then

bmap[i][j]=bmp.bmap[edge*i

-2][edge*j+edge+2];

end if;

end for j;

*output the graph g

end for * i;

END

As it is said, the image is arranged into hexagons

having different dimensions of edges.

Proposition 1

The total running time of a call of the procedure

Construct_Graph (Image I, edge) is O(n*m),

where “n” is the width and “m” is the height of the

digital image.

Proof

It can be observed that the first FOR loop of the

algorithm is executed at most once for a pixel of the

width of image. Hence, the total time spent in this

loop is O(n). The second FOR loop processes the

pixels on the height. Hence, the total time spent in

this loop is O(m). So, the total time spent in these

Secret

Key (K)

Authentication

Algorithms

Watermarked

Image (I’)

Watermark

(

)

Original

Image (I)

Analyzed

image (I’)

Watermark

retrieval

Watermark

message or

confidence

measure

Watermark

(W)

Original

Image (I)

Secret Key

(K)

AN ALGORITHM FOR AUTHENTICATION OF DIGITAL IMAGES

305

loops is O(n*m), because are processed all pixels of

the image at most once.

From previous statements is inferred that the

total running time of this procedure is O(n*m)

For introducing the watermarked key W will be

considered certain nodes from the graph (i, j), that

may be selected to represent a letter, a number or a

function. In these nodes are changed the three color

channels of the considered pixel depending on the

three color channels of the bottom pixel minus one

or another constant (const). It may consider a started

node having the coordinates (m0, n0) where it is

started the marking process and an ended node

where the process of marking is ended. In this way

the watermarked key is spread on entire image, not

only into a selected block of image (like in other

methods).

The algorithm for marking the image graph is

shown bellow:

PROCEDURE mark_graph (Graph bmap)

BEGIN

*choose 2 nodes in the graph

(m0,n0) and (m1,n1)

for * i->m0,n0

for * j ->m1,n1

change_color(i, j, const)

end for j;

end for i;

END

For reconstructing the marked image should be

verified only the graph’s nodes corresponding to the

selected key. If the marked pixel has the color in the

interval [min, max] with respect to the color of the

bottom pixel, then it will consider that this pixel was

marked in conformity with the given algorithm. The

values min, max resulted from a lot of experiments

or from the nature of the application.

Proposition 2

The total running time of a call of the procedure

Mark_Graph (Graph bmap) is O(n

), where “n ” is

the number of nodes of graph attached to the image.

Proof

It can be observed that the first FOR loop of the

algorithm is executed at most once for each node of

the graph. Hence, the total time spent in this loop is

O(n). The second FOR loop processes the pixels of

node which has the same color of its neighbor. The

inner FOR loop processes the nodes of unvisited

neighbor. So, the total time spent in these loops is

O(n

), because are processed all nodes of the graph

at most once. From previous statements is inferred

that the total running time of this procedure is O(n

)

3 EXPERIMENTAL RESULTS

There are a lot of transformation that can be done on

images (Lin, 2001), (Fei, 2004): rotation,

redimension, compression (transforming the image

to JPEG), cropping and of course the case in which

the image is not changed. Because it is not known

what transformation the user did, all these

transformations are verified one –by – one and the

percentage of similitude between the original image

and the verified one is returned. If the image is not

transformed, the algorithm presented below is

applied:

PROCEDURE detect_untransformed (Image

I,int edge,int w,int h, int percent)

BEGIN

*construct_graph (I,edge );

count=0;

for * i->m0,n0

for * j->m1,n1

if (color(bmap[i][j]) ==

color(bmap[i+1][j]) -1)

count = count +1;

end if;

end for j;

end for i;

*output percent of similitude

between the original marked

image and the image verified;

END

Also this algorithm may be applied if the image

is cropped. In this case it may be possible to lose

some marked pixels depending on the position

where the image was cropped.

In the next figure, the first image is the image

marked and the second one is the image marked and

cropped. The detection algorithm detects this

cropped image in percent of 88.88%.

Figure 3: Experiment 1.

In the case of image rotation (angle of 90, 180

and arbitrary), are verified all the image nodes

because the nodes’ position is changed. We search

the nodes for which all of the three color’ channels

have the values like the color’s channels of the

bottom pixel minus one (or a certain constant).

Before verifying these nodes, the image is

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dimensioned again to the initial dimensions at which

the image is marked.

In the next figure, the first image is the marked

image and the second is the image marked and

rotated by 30 degree.

Figure 4: Experiment 2.

PROCEDURE detect_rotated (Image I,int

edge,int w,int h, int percent,int

constant)

BEGIN

*redimension(I,w,h);

*construct_graph (I,edge );

count=0;

for * i->0,w

for * j->0,h

if (color(bmap[i+1][j]) -

constant<=color(bmap[i][j])

<= color(bmap[i+1][j])

+constant)

then count=count+1;

end if;

end for j;

end for i;

*output percent of similitude

between the original marked image

and the image verified;

END

By experiments, there resulted that in the case of

rotation by 90 and 180 degree, the constant is zero,

but in the case of rotation by arbitrary angle the

constant is very great (100).

The marked image was rotated with different

angles. From experiments resulted the following

percentage of similitude between the marked image

and the marked rotated image, as shown in the

following table.

Table 1: Results 1.

Rotation Angle Similitude Percent

30 33.33%

60 33.33%

90 100%

180 100%

The implemented authentication algorithm

entirely detects an image that was transformed to

JPEG, only if the image is compressed with a quality

of 100% and 80%. For image processes

(compression, decompression), was used ADOBE

PHOTOSHOP. The variable m0, n0, m1, n1, h, w

are known , being the same variables that are used in

the process of image marking. The color of pixels

arranged into nodes selected by us for marking the

image is changed because of transformations

supported by the image. Then the color of these

pixels is searched into a certain interval.

PROCEDURE detect_jpg (Image I,int

percent, int constant)

BEGIN

*decompressed(I);

*construct_graph (I, edge);

count=0;

for * i->m0,n0

for * j->m1,n1

if (color(bmap[i+1][j])

-constant <=color(bmap[i][j]) <=

color(bmap[i+1][j]) +constant)

then count = count +1;

end if;

end for j;

end for i;

*output percent of similitude

between the original marked image

and the image verified;

END

From experiments results that a good value for this

constant is 30. Using different degree of image

compression for JPEG, there are resulted the

following percents of similitude between the marked

image and the JPEG marked image.

Table 2: Results 2.

Quality Similitude Percent

100 100%

80 100%

60 33.33%

50 33.33%

30 0%

10 0%

In the case when we want to detect an image that

was enlarged the results are weaker:

AN ALGORITHM FOR AUTHENTICATION OF DIGITAL IMAGES

307

PROCEDURE detect_larged (Image I,

int edge,int w,int h, int percent,int

constant)

BEGIN

*redimension(I,w,h);

*construct_graph (I,edge);

count=0;

for * i->0,m

for * j->0,n

if (color(bmap[i+1][j]) -

constant <=color(bmap[i][j])

<= color(bmap[i+1][j])

+constant)

then count = count +1;

end if;

end for j;

end for i;

*output percent of similitude

between the original marked image

and the image verified;

END

From experiments results that a good value for

this constant is 70.

4 CONCLUSION

Watermarking, as opposed to steganography, has the

additional notion of robustness against attacks. Even

if the existence of the hidden information is known it

is difficult for an attacker to destroy the embedded

watermark, even if the algorithmic principle of

watermarking method is public. In cryptography,

this is known as Kerkhoffs law: - a cryptosystem

should be secure, even if an attacker knows the

cryptographic principles and methods used but does

not have the appropriate key (Hartung, 1999).

Meanwhile, the number of image watermarking

publications is too large to give a complete survey

over all proposed techniques.

Software watermarking is the process of

embedding a large number into a program so that:

(a) the number can be reliably retrieved after the

program has been subjected to program

transformations, (b) the embedding is imperceptible

to an adversary and (c) the embedding does not

degrade the performance of the program.

The method developed above satisfies the

necessary requests for the authentication technique

and the series of presented transformations accounts

for the fact that it resists possible attacks. The

method is easy to implement and the experimentally

determined robustness shows that it can be used

without fear of being detected or changed. In

addition, the authentication method can be easily

used for marking of medical digital images with

other information besides those for watermarking.

The watermarked image is not a new one because

the number of the transformed pixels is very little (n

< < number of the image pixels, where ‘n’ is the

number of nodes of virtual graph). In addition, the

pixels of the watermarking are slightly changed thus

the human eye cannot discern them.

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