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CHAPTER-1

INTRODUCTION

1.1 MOTIVATION AND OVERVIEW

Digital cameras differ from traditional cameras in many ways. But the basic

difference is that they use solid state image sensors to convert light to digital pictures

rather than capturing the image on film. Digital imaging has actually been around for

a long period of time, but it has been used for other purposes.

The history of digital technology began very early. NASA began dealing with digital

imaging technology as far back as the 1960s, just as it did with many inventions that

have become public domain, and NASA used it to convert signals from analogue to

digital. Very soon, other governmental sectors saw the opportunities and advantages

of this emerging digital technology and they began a similar program involving spy

satellites. Today similar applications are available for free to anyone with internet

access. For example Google's satellite maps show the whole world and even the

moon.

1.12 DEVELOPMENT OF DIGITAL CAMERAS

The true digital cameras did not simply emerge as a new consumer product. There

was several other products developed fist, which led to its creation.

Digital cameras as we know them today first became available for consumers around

the mid-70s. At that time, Kodak developed a number of solid state image sensors

which converted available light into digital images. The target customers for the new

Kodak digital cameras were both professionals and hobbyists.

From that point on the camera industry began to develop faster and the ability to

connect to the home computer to download pictures was introduced. The development

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was combined with software to manipulate and edit pictures, and special printers

dedicated to digital photography.

1.13 HOW DO DIGITAL CAMERAS WORK?

In the digital world, data, or information, is represented by strings of 1's and 0's. In

this case these digits translate to the individual pixels or basic units that combine to

make up the image you see. When the capture button on the camera is pressed, a

charge coupled device (also known as a CCD) creates an electron equivalent of the

captured light which in turn ends up converting the pixel value into a digital value.

Each picture is stored in the camera's memory until it is downloaded to its destination,

usually a computer or a CD. Usually, the form of camera memory is a memory card

which can be replaced. Indeed, this is one of the great advantages over traditional

cameras – you don’t have to buy films.

1.14 IMPORTANT FEATURES TO LOOK FOR IN A DIGITAL CAMERA

Resolution is one of the most important features and in many cases it is one of the

top features that determine a camera's price. Resolution is a measure of detail that a

specific camera will capture. The basic unit of measurement when referring to digital

camera resolution is the pixel. The higher the number of pixels the better the is

camera, because a higher level of detail is captured.

Digital cameras are rated in megapixels (millions of pixels). A 1.0 megapixel camera

is considered not to be of quality while a 5.0 megapixel camera is often used in

professional digital photography when creating studio grade portraits or taking

pictures. The lens is very important when it comes to digital cameras because it

focuses directly into what you intend to use the digital camera for. A lens that has a

fixed focus and fixed zoom should just be used for simple snapshots. Zoom lenses

come in two forms: the optical zoom lens and the digital zoom lens. The optical

zoom is preferable because it zooms by changing the actual focal length of the lens

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whereas the digital zoom uses an interpolation algorithm to zoom; it “infers”

information by evaluating neighbor information. This results in a grainy photo.

Replaceable lenses are found on many higher end cameras. The good thing about

them is that they increase the camera's versatility. There can be found: zoom lenses,

close-up lenses, color lenses for effects, and panoramic lenses.

How many useful digital camera accessories are available for a particular model? As

already mentioned above, some cameras, like Kodak, offer a docking system which

not only is the interface to the computer but also doubles as a battery charger when

the camera is not in use, ensuring that it starts off with a full charge when needed.

Choosing a digital camera is not easy, but if you have decided which particular

model you need, you will enjoy taking digital pictures wherever you go to: on

vacation, at a family dinner, at a party with friends, at school, etc.

1.2 LITERATURE SURVERY

The technology that is being used in this topic is image processing. This topic

mainly deals with the method to detect a hidden camera and the ways by which we

can neutralize it. An image can be defined as a two dimensional

function,f(x,y),where x and y are spatial coordinates and amplitude of ‘f’ at any

points(x, y) is called intensity. The field of image processing refers to processing

refers to processing digital images by means of a digital computer. Image

processing can be used in the field like x-ray imaging, gamma imaging, imaging in

the microwave band etc.

1.21 IMAGE SEGMENTATION

Segmentation is a process that partitions an image into regions. If we wish to segment

an image based on color, and, in addition,we want to carry out the process on

individual planes ,it is natural to think first of HSI color space because color is

conveniently represented in the hue image. Segmentation is one area in which better

results can be obtained by using RGB color vectors.

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1.22 THRESHOLDING

Because of its intuitive properties, simplicity of implementation, and computational

speed, image Thresholding enjoys a central position in applications of image

segmentation . Consider an image f(x, y), composed of light objects on a dark back

ground, in such a way that object and background pixels have intensity values grouped

into two dominant modes. At any point (x, y) in the image at which f(x, y)>T is called

an object point ; otherwise the point is called a background point. When T is a constant

applicable over an entire image ,the process given in this is referred to as global

Thresholding. When the value of T changes over an image, we use the tem variable

Thresholding. The term local or regional Thresholding is used sometimes to denote

variable Thresholding in which value of T at any point (x,y) in aimage depends on

properties of a neighborhood of (x,y).

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CHAPTER-2

EXISTING SYSTEM

2.1 INTRODUCTION

A new method for the problem of digital camera identification from its images based

on the sensor’s pattern noise. For each camera under investigation, we first determine

its reference pattern noise, which serves as a unique identification fingerprint. This is

achieved by averaging the noise obtained from multiple images using a denoising

filter. To identify the camera from a given image, we consider the reference pattern

noise as a spread spectrum watermark, whose presence in the image is established

using a correlation detector. Experiments on approximately 320 images taken with

9consumer digital cameras are used to estimate false alarm rates and false rejection

rates. Additionally, we study how the error rates change with common image

processing, such as JPEG compression or gamma correction.

2.2 EXPLANATION

As digital images and video continue to replace their analogcounterparts, the

importance of reliable, inexpensive, and fast identification of digital image origin

will only increase. Reliable identification of the device used to acquire particular

digital image would especially prove useful in the court for establishing the origin of

images presented as evidence. In the same manner as bullet scratches allow forensic

examiners to match a bullet to a particular barrel with reliability high enough to be

accepted in courts, a digital equivalent of bullet scratches should allow reliable

matching of a digital image to a sensor. In this paper, we propose to use the sensor

pattern noise as the tell-tale “scratches” and show that identification is possible even

from processed images.

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We have developed a new approach to the problem of camera identification from

images. Our identification method uses the pixel non-uniformity noise which is a

stochastic component of the pattern noise common to all digital imaging sensors

(CCD, CMOS, including Fovea™ X3, and JFET).The presence of this noise is

established using correlation as in detection of spread spectrum watermarks. We

investigated the reliability of camera identification from images processed using

JPEG compression, gamma correction, and a combination of JPEG compression and

in-camera resampling. Experimental results were evaluated using FAR and FRR error

rates. We note that the proposed method was successful in distinguishing between two

cameras ofthesame brand andmodel.Techniques, are described here may help

usalleviate the computational complexity of brute force searches by retrieving some

information about applied geometrical Operations. The searches will, however,

inevitably increase the FAR. We would like to point out that the problem of camera

identification should be approached from multiple directions, combining the evidence

from other methods, such as the feature-based identification , which is less likely to be

influenced by geometrical transformations.

2.3 FORGING AND MALICIOUS PROCESSING

Since camera identification techniques are likely to be used in the court, we need to

address malicious attacks intended to fool the identification algorithm, such as

intentionally removing the pattern noise from an image to prevent identification or

extracting the noise and copying it to another image to make it appear as if the image

Was taken with a particular camera. We distinguish two situations: 1) the attacker is

informed and has either the camera or many images taken by the camera or 2) the

attacker is uninformed in the sense that he only has access to one image.

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CHAPTER-3

PROPOSED SYSTEM

3.1 INTRODUCTION

The system locates the camera, and then neutralizes it. Every digital camera has an

image sensor known as a CCD, which is retro reflective and sends light backing

directly to its original source at the same angle. Using this property and algorithms of

image processing the camera is detected. Once identified, the device would beam an

invisible infrared laser into the camera's lens, in effect overexposing the photo and

rendering it useless. Low levels of energy neutralize cameras but are neither a health

danger to operators nor a physical risk to cameras. Digital cameras differ from

traditional cameras in many ways. But the basic difference is that they use solid state

image sensors to convert light to digital pictures rather than capturing the image on

film. Digital imaging has actually been around for a long period of time, but it has

been used for other purposes.

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CCD

Test Image

Recorder

Scanning

Infrared Emitter

Image

Processing Unit

Camera Locator

Infrared Laser

Beam

Projector

Overexposure

IR Laser Beam

Timing and Control

DETECTOR UNIT DISABLING UNIT

3.2 DESIGN AND ARCHITECTURE

Fig 3.1BLOCK DIAGRAM

3.3 RETRO REFLECTION BY CCD

A retro reflector is a device or surface that reflects light back to its source with a

minimum scattering of light and at same angle. An electromagnetic wave front is

reflected back along a vector that is parallel to but opposite in direction from the

wave's source. The device or surface's angle of incidence is greater than zero. The

CCD of the camera exhibits this property due to its shape. This forms the principle for

this device.

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Fig 3.2: Retro reflection by CCD

3.4 CAMERA DETECTION

3.4.1SCANNING

The entire area to be protected is scanned by using infrared light. Infrared LED is

used for producing them. The circuitry required for producing infrared beams are

simple and cheap in nature. The scanning beams sweep through the vertical and

horizontal direction of the area, to ensure no camera escapes from the device.

3.4.2 WAVELENGTH

The infrared beam used here has the center wavelength of 800-900 nm. This

wavelength falls under the near infrared classification. The reason for choosing near

infrared are the molar absorptivity in the near IR region is typically quite small and it

typically penetrate much farther into a sample than mid infrared radiation so that the

retro reflections would be of high intensity. The generation of NIR is achieved using

IR LED. Due to the retro reflective property of the CCD the part of the light gets retro

reflected by it and the infrared beam does not have any effect on the other objects hit

the area other than the CCD.

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Fig 3.3: Plot of Reflectance vs. Wavelength of Near IR Standard

3.4.3 TEST IMAGE CAPTURE

The area being scanned by the infrared beams are simultaneously recorded. The

preprocessing image being acquired is called as the test image. It forms the basis of

the further steps of the process. The test image is obtained by use of high resolution

camcorders. The response of the test image capture should be very fast in order to

sense even a small change of position of the camera. The camcorder should have a

wide angle of capture so that it can capture a wide test image to cover the entire area.

The retro reflected beams also have the same properties of the near IR. Therefore,

they are visible to the camcorders and invisible to human eyes.

3.5 IMAGE PROCESSING

It is most important aspect of the device. The raw image for image processing is the

test image being streamed lively. The detection of the camera is accomplished in this

stage only. The image processing for detection can be done in two steps.

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We have coded an algorithm in Mat lab software to perform the image processing

operation.

3.5.1 DETECTION OF RETRO REFLECTING AREA

The camera is detected by the differentiation of the retro reflecting area from the rest

of the test image. The camera lens also appears red in color and the rest part appears

normal. This key point is used for differentiation.

3.5.2 THRESHOLDING

During the Thresholding process, individual pixels in an image are marked as

“object” pixels if their value is greater than some threshold value (assuming an object

to be brighter than the background) and as “background” pixels otherwise. The

separate RGB Components are determined and a threshold value is set.

1. An initial threshold (T) is chosen; this can be done randomly or according to

any other method desired.

2. The image is segmented into object and background pixels as described above,

creating two sets:

1. G1 = {f(m , n):f(m ,n)>T} (object pixels)

2. G2 = {f(m ,n):f(m ,n)T} (background pixels) (note, f(m ,n) is the value

of the pixel located in the m th

column, nth

row)

3. The average of each set is computed.

1. m1 = average value of G1

2. m2 = average value of G2

4. A new threshold is created that is the average of m1 and m2

1. T’ = (m1 + m2)/2

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5. Go back to step two, now using the new threshold computed in step four, keep

repeating until the new threshold matches the one before it (i.e. until

convergence has been reached).

This iterative algorithm is a special one-dimensional case of the k-means clustering

algorithm, which has been proven to converge at a local minimum—meaning that a

different initial threshold may give a different final result.

K-means clustering is a method of vector quantization, originally from signal

processing, that is popular for cluster analysis in data mining. K-means clustering

aims to partition n observations into k clusters in which each observation belongs to

the cluster with the nearest mean, serving as a prototype of the cluster. This results in

a partitioning of the data space into Voronoi cells. K-means clustering tends to find

clusters of comparable spatial extent, while the expectation-maximization mechanism

allows clusters to have different shapes.

Demonstration of the standard algorithm

Fig 3.4: K initial "means" (in this case k=3) are randomly generated within the data

domain (shown in color).

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Fig 3.5: K clusters are created by associating every observation with the nearest mean.

The partitions here represent theVoronoi diagram generated by the means.

Fig 3.6: The centroid of each of thek clusters becomes the new mean.

Fig 3.7: figure of convergence.

As it is a heuristic algorithm, there is no guarantee that it will converge to the global

optimum, and the result may depend on the initial clusters. As the algorithm is usually

very fast, it is common to run it multiple times with different starting conditions.

However, in the worst case, k-means can be very slow to converge: in particular it has

been shown that there exist certain point sets, even in 2 dimensions, on which k-

means takes exponential time, that is 2Ω(n)

, to converge. These point sets do not seem

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to arise in practice: this is corroborated by the fact that the smoothed running time

of k-means is polynomial.

The "assignment" step is also referred to as expectation step, the "update step"

as maximization step, making this algorithm a variant of the generalized expectation-

maximization algorithm.

3.5.3 COMPLEXITY

Regarding computational complexity, finding the optimal solution to the k-means

clustering problem for observations in d dimensions is:

NP-hard in general Euclidean space d even for 2 clusters

NP-hard for a general number of clusters k even in the plane

If k and d (the dimension) are fixed, the problem can be exactly solved in

time O(ndk+1

log n), where n is the number of entities to be clustered

3.5.4 COLOR SEGMENTATION

We need to detect only the red infrared part of the image. This is done by means of

color segmentation. The RGB Components are filtered separately and finally the red

area is detected. The following algorithm was used for the purpose

Img=imread('sample.jpg');

%imshow(img)

img=imfilter(img,ones(3,3)/9);

%img=imresize(img,0.1);

%Decomposetoseparatecolorcomponents

xr=img(:,:,1);

[N,M]=size(img);

m=4;

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w=1/m;

F=fftshift(fft(double(img)));

fori=1:N

forj=1:M

r2=(i-round(N/2))^2+(j-round(N/2))^2;

if(r2>round((N/2*w)^2))

F(i,j)=0;

end;

end;

end;

Idown=real(ifft2(fftshift(F)));

3.6 DISABLING OF DIGITAL CAMERA

3.6.1OVEREXPOSE

Once the camera lens has been located it has to be over exposed. A photograph may

be described as overexposed when it has a loss of highlight detail, i.e. when the bright

parts of an image are effectively all white, known as "blown out highlights". Since the

infrared beam is of high intensity rather than the other light incident on the lens from

the image, the camera tends to be overexposed. The auto focusing mechanism of the

camera adjusts the position of the lens to focus on the infrared beam. This causes non-

focusing of the camera on the image that is to be prevented from capturing.

Example of overexposure by infrared laser

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Fig 3.9 : Effect of over-exposure

3.7 SURROUNDING ADAPTIVE OVEREXPOSURE BEAM WAVELENGTH

The wavelength of the infrared beam being emitted intermittently is not constant. The

wavelength is altered according to the lighting nature of the environment. This is

achieved by using a sensor which is based on photo detector. If the surrounding is

dark the beam of center wavelength of 900-980 nm is emitted. . If the surrounding is

bright the beam of center wavelength of 800-900 nm is emitted.

Fig3.8 : Normal exposure

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3.8 OBSERVATIONS

Fig 3.10:shows the photo captured normally without using the camera disabling

device

Fig 3.11: shows the photo captured after using the camera disabling device

It is observed that the image quality has been decreased to a great extent. This could

be used to diminish the clarity and the visibility of the image being captured.

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CHAPTER-4

APPLICATION

4.1SIMPLIFIED DESIGN FOR USE IN THEATRES

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Fig 4.1:Design used in theatre

The film industry losses about 3 billion dollar a year due to movie piracy. This is the

method that can deployed to prevent piracy.Infra-red light emitting diodes are placed

behind the theatre screen. The beams are emitted intermittently. The wavelength of

the beam and the timing is varied continuously using the timing and control unit. This

beam can be detected by the camera CCD sensors. Since the beams is of high

intensity and narrow the auto focusing feature the camera gets detoriated . So the

video which is being tried to capture on the camera falls out of focus. The quality of

the image therefore obtained is of poor clarity. Thereby, the aim of pirating the movie

is destructed. Thence the infrared beam does not fall within the visible range of the

human sight it remains invisible to the human eyes. Therefore, the overexposure beam

does not affect the movie being played on the screen.

1 – Camera Disabling Device

2- Theatre Screen

3- Camera used for piracy

4- Theatre Projector

4.2 Merits

The circuitry and devices used for this technique are simple in nature. The type of

radiation is proven to be not harmful to humans. It can be implemented easily in any

type of rooms, buildings, theatres etc. without any alteration to the existing area.

Since the method uses a low cost technology it can be implemented at a

comparatively less expense.

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CHAPTER-5

CONCLUSION

The device explained above can thus be used to disable and detect hidden cameras

and provides protection to all surroundings. The device explained above can prove to

be essential to all environments like theatres, lockers, private areas, anti-espionage

systems, defense secrecy etc. This technology if developed to a good extent it would

be of great help prevents piracy, maintain national secrecy in etc.

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REFERENCES

[1]. “Optical principles and technology for engineers “- James EStewart

[2]. “Infrared optics and zoom lenses“ By Allen Mann

[3]. “Digital image processing using Matlab“By Rafael C.González, Richard

Eugene Wood

[4]. Blythe, P. and Fridrich, J: “Secure Digital Camera,” “ Digital Forensic”.

[5]. Digital Image processing: algorithms and systems San Jose,Jaakko Astola,

Karen Egiazarian, Edward R. Dougherty