AN ENS EMBLE APPROACH FOR IMAGE AUTHENTICATION US ING LOCAL AND GLOBAL

FEATURES

ABSTRACT

An ensemble approach is developed to

detect the insertion of forged objects in an image

using Salient Region Comparison, and to determine

the background of an object, in case if an image is

cropped or damaged. The test image is compared

with that of a reference image and divided into many

salient regions; each salient region is then compared

to detect the insertion of any objects in the test image.

In addition, if an object of an original image is

cropped, using the technique Individual Pixel Value

Comparison the background of an image can be

detected. In order to achieve accuracy of the cropped

image the homogeneous and inhomogeneous regions

are found which helps in estimat ing the structure of

the removed/cropped objects.

INTRODUCTION

1.1 OVERVIEW

With the widespread use of image edit ing

software, ensuring credibility of the image contents

has become an important issue. Saliency comparison

is a technique that extracts a short sequence from the

image to represent its contents, and therefore can be

used for image authentication. Image forgery by

insertion of objects is a very serious issue faced

today. Some objects are been inserted by

unauthenticated users so as to forge the image. This

in turn creates chances of misinterpreting a wrong

decision in the area of forensic science. Any digital

image, or even non-digital images in some cases,

present details to a viewer that is not necessarily

readily d istinguishable from reality. Basically, an

individual often finds it difficu lt to determine

whether an image has been digitally altered or not.

Very few ways exist of distinguishing a picture that

has been altered from a picture that has undergone no

such alteration. Because of this fact, viewers of

images are often rightfully suspicious of the details

conveyed by images. By being too suspicious

however, one might neglect to realize the beauty or

significance of an image that contains no digital

alteration, and this beauty or significance might have

been comprehended if one simply was not so

suspicious of image details. In order to avoid such

wrong impacts an ensemble method is proposed to

detect the insertion of forged images. Initially the

image is divided into many salient regions. The

salient regions are computed for both the original and

test images. Each salient reg ion of the test and

reference images is compared to detect the insertion

in saliency map. In addition re-filling is done to

detect the background. Re-filling is very important

criteria to detect and pretend what the background

would be if the object is cropped or hided. In forensic

science some cases becomes forced to detect the

background. For such cases the proposed feature is

done by hiding or removing the homogeneous and

the non-homogeneous mixture of the images.

1.2 PROPOS ED METHOD

An ensemble approach is developed to

detect the insertion of forged objects in an image

using Salient Region Comparison, and to determine

the background of an object, in case if an image is

cropped or damaged. The test image is compared

with that of a reference image and divided into many

salient regions; each salient region is then compared

to detect the insertion of any objects in the test image.

In addition, if an object of an original image is

cropped, using the technique Individual Pixel Value

Comparison the background of an image can be

detected. In order to achieve accuracy of the cropped

image the homogeneous and inhomogeneous regions

are found which helps in estimat ing the structure of

the removed/cropped objects.

1.3 ADVANTAGES OF PROPOS ED METHOD

It consumes less time.

The insertions of the forged images are

identified along with the histograms.

Determining the accurate values.

Refilling is beneficial, to suspect the

back ground.

This is highly applicable in the areas of

forensic science.

ARCHITECTURE DIAGRAM

PROPOS ED SCHEME

A .IMAGE INS ERTION IDENTIFICATION

The proposed image salient reg ion detection

involves the following techniques

A.1 Salient region

Salient region ext racts features of objects in

images that are distinct and representative. It can also

be set as the objects in the image that attracts the

visual attention of the user. Here all the objects of the

image are taken into account as individual salient

region so as to ensure the minute insertion of objects.

The image is given a certain thresh hold value on

which the regions are categorized. The higher the

thresh hold value larger number of salient reg ions can

be generated. The steps undergone in salient region

detection are shown in the Fig 1. Detection of salient

image regions is useful for applications like image

segmentation, adaptive compression, and region-

based image retrieval. Most saliency .

Detection methods take a similar center-

versus-surround approach. One of the key decisions

to make is the size of the neighborhood used for

computing saliency.

Fig : 1 salient reg ion detection

A.2 Conversion of RGB to gray scale image

A grayscale digital image is an image in

which the value of each pixel is a single sample, that

is, it carries only intensity information. Images of this

sort, also known as black-and-white, are composed

exclusively of shades of gray, varying from black at

the weakest intensity to white at the strongest.

Grayscale images have many shades of gray in

between. Grayscale images are also

called monochromatic, denoting the presence of only

one (mono) co lor (chrome). The intensity of a pixel is

expressed within a given range between a min imum

and a maximum, inclusive. This range is represented

in an abstract way as a range from 0 (total absence,

black) and 1 (total presence, white), with any

fractional values in between.

Fig : 2 Grayscale conversions

Fig 2 depicts gray scale conversion. For

many applications of image processing, color

informat ion doesn't help us identify important edges

or other features. Color images include complexity in

code format ion. For learning image processing, it's

better to understand grayscale processing first and

understand how it applies to mult ichannel processing

rather than starting with full color imaging and

missing all the important insights.

A.4. Contrast limited adaptive equalization

The contrast of the gray scale

image is increased so that the dim objects can be

viewed clearly. Contrast is defined as the separation

between the darkest and brightest areas of the image.

Increasing the contrast increases the separation

between the dark and bright, making shadows darker

and highlights brighter. Decreasing contrast makes

the shadows highlights down to make them closer to

one another. Adding contrast usually adds “pop” and

makes an image look more v ibrant while decreasing

contrast can make an image look duller which is

shown in Fig 3. It is clear that the dimmer objects

gets brightened so that the forgery occurred can be

found to a greater extent.

Fig : 3 Contrast based adaptive equalization

A.5. Background subtraction

It also known as Foreground Detection is a

technique in the fields of image

processing and computer vision wherein an image's

foreground is extracted for further processing (object

recognition etc.). Generally an image's regions of

interest are objects (humans, cars, text etc.) in its

foreground. After the stage of image preprocessing

(which may include image denoising etc.) object

localization is required which may make use of this

technique. Background subtraction is a widely used

approach for detecting moving objects in videos from

static cameras. The rationale in the approach is that

of detecting the moving objects from the difference

between the current frame and a reference frame,

often called “background image”, or “background

model. The background detected objects are

differentiated from each other by watershed

algorithm shown in the Fig 4. This segregates each

object from its relative objects. It takes into account

of two spatial dimensions and one brightness

dimension. Watershed makes the user’s view clearer.

A.6. Image histogram

An image h istogram is a type of histogram that

acts as a graphical representation of the tonal

distribution in a digital image. It p lots the number of

pixels for each tonal value. By looking at the

histogram for a specific image a viewer will be able

to judge the entire tonal distribution at a glance.

Image histograms are present on many modern dig ital

cameras. Photographers can use them as an aid to

show the distribution of tones captured, and whether

image detail has been lost to blown-out highlights or

blacked-out shadows.

The horizontal axis of the graph represents the

tonal variations, while the vertical axis represents the

number of pixels in that particular tone. The left side

of the horizontal axis represents the black and dark

areas, the middle represents medium grey and the

right hand side represents light and pure white areas.

The vertical axis represents the size of the area that is

captured in each one of these zones. Thus, the

histogram for a very dark image will have the

majority of its data points on the left side and center

of the graph. Conversely, the histogram for a very

bright image with few dark areas and/or shadows will

have most of its data points on the right side and

center of the graph shown below in Fig 5.

Fig :5 Histogram

B. BACKGROUND REPLACEMENT

B.1 Homogeneous region and inhomogeneous

region

General idea of a h istogram to the homogeneity

domain is to detect the homogeneous region.

Uniform reg ions are identified via multilevel

thresholding on homogeneity histogram regions.

While processing the homogeneity histogram, both

local and global information is taken into

consideration. This is particularly help ful in taking

care of small objects and local variation of color

images. An efficient peak-finding algorithm is

employed to identify the most significant peaks of the

homogeneous region. Homogeneous region is define

as detecting the inner part of the forged object which

means complete inner region of the detected image or

object will be displayed using homogeneous

detection algorithm .All the similar images or objects

which is been detected as forged area will be

displayed with perfect content. Inhomogeneous

region is defined as the outline or border of the

homogeneous region which is detected earlier.

Inhomogeneous region is detected to identify the

exact border of the forged image or object.

Considering both the homogeneous region and the

inhomogeneous exact forged image or an object can

be identified shown in Fig 6.

Fig : 6 homogeneous and inhomogeneous region

B.2 Threshold value

Threshold value is the maximum value on

which a process can be carried out; here threshold

value indicates the maximum value upto which the

image can be divided into many salient regions. You

can also specify actions to be taken when a second

value (called the reset value) is reached. For some

metrics, it is appropriate to specify a reset value,

which resets the threshold and allows it to be

triggered again when the trigger value is reached. For

those thresholds, you can specify a command to be

run when the reset value is reached. For other metrics

(such as the File Status metric and the Text metric on

file monitors, and any message set on a message

monitor), you can specify to automatically reset the

threshold when the trigger command is run.

Thresholds are triggered and reset based on the value

at the time the metric collection is made. Specifying a

higher number of collection intervals in the Durat ion

field helps to avoid unnecessary threshold activity

due to frequent spiking of values.

On the New Monitor - Metrics page, the

threshold tabs provide a place for you to specify a

threshold value for each metric that you have selected

to monitor. For example, if you are creating a job

monitor, you can set your threshold values in the

following ways depending on the type of metric you

have selected. Every threshold has a current value,

which is displayed by the threshold indicator. The

value ranges from 0 - 100% and the position of the

bar in the indicator shows this value. The value of the

threshold is defined by two factors: the trigger, and

how long the threshold must have triggered for.

The number of salient region increases rapidly

by increasing the threshold value. For every increase

of 25 integers in threshold value the number of

salient regions increases by an average of 9 which is

depicted in the below in the table 1.

Threshold Value Detected Salient Regions

25 36

50 45

75 54

100 63

Table 1 threshold value for salient reg ion detection

Threshold value is the maximum value on

which a process can be carried out; here threshold

value indicates the maximum value upto which the

image can be divided into many salient regions. You

can also specify actions to be taken when a second

value (called the reset value) is reached. For some

metrics, it is appropriate to specify a reset value,

which resets the threshold and allows it to be

triggered again when the trigger value is reached. For

those thresholds, you can specify a command to be

run when the reset value is reached.

B.3 Filtering the image

Filtering the image is the process of removing

the noise from an image.

The algorithm proposed is an extension of Lee's local

statistics method modified to utilize local gradient

informat ion. It does not require image modeling, and

it will not smear edges and subtle details. For both

the additive and multiplicative noise cases, the local

mean and variance are computed from a reduced set

of pixels depending on the orientation of the edge.

Consequently, noise along the edge is removed, and

the sharpness of the edge is enhanced. UINTA

automatically discovers the statistical properties of

the signal and can thereby restore a wide spectrum of

images. The bilateral filter is a nonlinear filter that

does spatial averaging without smoothing edges; it

has shown to be an effective image denoising

technique. An important issue with the application of

the bilateral filter is the selection of the filter

parameters, which affect the results significantly. For

practical applications when the noise variance is

spatially varying and unknown, an adaptive filtering

algorithm is developed. Experiments show its good

potential for processing real-life images. Examples

on images containing 256×256 pixels are given to

substantiate the theoretical development. Filtering is

used to remove the unwanted noise, frequency etc.,

The filter thus removes the unwanted noise at a larger

scale by doing so the image will be free of

noising...clear view of the image is obtained.

C.CONCLUS ION AND FUTURE

ENHANCEMENT

The proposed method clearly detects the

insertion of objects and identification of tampered

location in the saliency map of the image. The

number of salient region is increased for ease

identification of insertion. Even the insertion of

minute objects in the image can be detected

accurately. It provides the way for the detection of

background of the image when the object in the

image is cropped. It is applicable also when the

objects in the image is damaged because of any

external noise. Gaussian vectors are found which

depicts accuracy in results. This paper is highly

beneficial in the areas of forensic science and crime

investigation. Also when secure images have to be

sent this technique can be used. In future the paper

can extended by suspecting the background of color

images and to identify the other forgeries such as

removal and color modification in the saliency of the

image.

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