Digital Image Forgery Detection.pptx

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Digital Image Forgery Detection

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Presentation Division

Introduction Forgery Detection

Region Duplication Conclusion


Types of Forgery

Forgery Detection Mechanisms

High Precision Rotation Angle Estimation For Copy Move.

Explaination Rotation Angle

Calculation Variance

Estimation Algorithm

Discrete Cosine Transform

Walsh Transform

Hybrid Wavelet Transform

Results Future Works References

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Alteration of the semantic components of a digital image. Removing Contents from the image Adding Data to the image

Types of Forgery Image Retouching Image Splicing (Copy-Paste) Image Cloning (Copy-Move)

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Image Retouching

One of the oldest types of image forgery

Image features are tampered with.

Used to enhance or reduce digital image features.

Considered less dangerous type of image forgery.

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Image Splicing (Copy-Paste)

Fragments of 2 or more images are combined to form an image.

This operation is fundamental in digital photo montaging and in turn is a mechanism for image forgery creation.

Image splicing technique may change the visual message of digital images more aggressively than image retouching.

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Image Cloning (Copy-Move)

Considered as a special case of image splicing, where the tampering occurs within a single image and no need for multiple images.

Part of the image is copied and then pasted in a desired location within the same image.

The purpose of such tampering is to duplicate or conceal a certain object in that image.

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Image Cloning

Blurring is usually used to reduce the expected irregularity along the border of the pasted regions.

The similarity of texture, color, noise and other information inside the image make it very difficult to detect this kind of tampering via visual inspection.

Moreover, performing of post-processing operations such as blurring, adding noise and JPEG compression or geometric operations such as scaling, shifting and rotation increase the hardness of detection task.

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Forgery Detection Mechanisms

Can be Classified into Two Types Active Methods

Passive Methods

Active Methods Hidden Information inside the Digital Image. Done at the time of Data Acquisition or before

disseminated to the public. Embedded information can be used to identify the

source of such image or to detect possible modification to that image.

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Forgery Detection Mechanisms(Active Methods)

Two Major Types

Digital Signature

Digital Watermarking

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Forgery Detection Mechanisms(Passive Methods)

Use traces left by the processing steps in different phases of acquisition and storage of digital images.

These traces can be treated as a fingerprint of the image source device.

Passive methods work in the absence of protecting techniques.

They do not use any pre-image distribution information inserted into digital image.

They work by analyzing the binary information of digital image in order to detect forgery traces, if any

Limitation is the number of false positives.

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High Precision Rotation Angle Estimation for Rotated Images

Paper addresses the detection of “copy-move”(cloning) technique

As discussed before cloning detection becomes harder when the forger uses geometric alterations like scaling, rotation & shifting.

Particularly addresses the Rotation transformation.

This paper proposes a novel blind image rotation detection algorithm with high precision rotation angle estimation

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S, t=pixel coordinates in the rotated image I.

= weighted value.

I= Original image

I’=Intermediate Image

I”= Rotated Image

𝐼 𝑠 , 𝑡} = sum from { =− } to { } {sum from { =− } to { } {left none ( { } rsub { , } rsup {′ } + , { } rsub { , } rsup {′ } + ) ′( { } rsub { , } rsup {′ } + , { } rsub { , } rsup {′ } + right )}𝑛 𝑁 𝑁 𝑚 𝑁 𝑁 𝜑 𝑖 𝑠 𝑡 𝑛 𝑗 𝑠 𝑡 𝑚 𝑰 𝑖 𝑠 𝑡 𝑛 𝑗 𝑠 𝑡 𝑚 ¿


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α = horizontal distance rotated image I′′ & intermediate image I′

β = vertical distance.

R and S are constant(translation)


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For a single pixel, we have:


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Plot of horizontal distance vector and its spectrum at

Plot of peak frequency of distance vector against all . Frequency is normalized to.


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Resolution Total Images Correct Images Correct Rate

500 486 97.2%

500 480 96.0%

500 471 94.2%

500 459 91.8%

500 438 87.6%


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Experiment results. 1st column: three images rotated at respectively; 2nd column: theoretical pixel variance spectrum for the rotated images; 3rd column: actual pixel variance spectrum for the rotated images.


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Conclusion In this paper, propose a blind image rotation angle

estimation method is proposed by exploring the periodicity of pixel variance of rotated images.

Experiment results show that this method works well for rotation angles larger than , but not as good for smaller rotation angles.

The method can be used in areas like copy-paste image forgery detection. In the future, the author plans to modify the algorithm to improve the correct rate of small rotation angle estimation.


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Region Duplication Forgery Detection using Hybrid Wavelet Transforms

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Starts by dividing the M×N suspicious image into small overlapping blocks.

This step is achieved by sliding a window of size B×B from the upper left corner to the lower right corner one pixel each time.

The quantized DCT coefficients are extracted from each block and used to represent the features of these blocks.

The quantized DCT coefficients are stored as one row in a matrix A of (M-B+1) × (N-B+1) rows and B× B columns, where B× B is the block size.

Two identical rows in the matrix A, correspond to two identical blocks in the suspicious image.

Discrete Cosine Transforms

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Region Duplication Forgery Detection using Hybrid Wavelet TransformsHadamard Walsh Transforms

The Product of a Boolean Function and a Walsh Matrix is a Walsh Spectrum

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Example of Copy-Move Forgery, (a) Original Image (b) Forged Image

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Thank you

Digital Image Forgery Detection.pptx

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