passive techniques for detection of tampering in images by surbhi arora and sarthak pahwa -jiit...

PASSIVE

TECHNIQUES

FOR DETECTION

OF TAMPERING IN IMAGES

Enrollment No.-10104690Sarthak PahwaEnrollment No.-10104704Surbhi AroraSupervisor-Manish Kumar Thakur

INTRODUCTION

Today manipulation of digital images has become easy due to powerful computers, advanced photo-editing software packages and high resolution capturing devices. Verifying the integrity of images and detecting traces of tampering without requiring extra prior knowledge of the image content or any embedded watermarks is an important research field.

Types of Tampering

Copy – paste: Copying a portion of another image and pasting in our image .

Copy- move: Copying a portion of our own image and pasting it in the same image.

Cut – Paste: Cutting a portion of our image and pasting it elsewhere in the same image .

Cut: Cutting a portion of our image.

Copy move

Copy Paste

Cut

Cut Paste

Approaches taken :

(1) Rule Based

The result depends a set of rules implied by the algorithm

(2) Training Based

The result depends on the training of a set of training images and comparing those trained images with the test images.

Rule Based

Exact Match The exact pixel values of the sliding block are taken and compared.

Robust Match Similar to exact match with the only difference being that the values taken are quantized DCT co-efficients than exact pixel values .

Speeded Up Robust Features with a thresholdSURF finds key points and single linkage with a threshold to match key points on the basis of the features extracted from SURF.

Exact Match and Robust Match Results :

Test Image

Result of Robust Match Result of Exact Match

SURF with a threshold results :

Result of SURF with single linkage algorithm

Training BasedBlock Intensities

Intensities of 32*32 blocks of the image

HU moments from DFT of sub bands of DWT of an image

The image is first subjected to DWT which gives us 13 sub bands on which DFT is performed and Hu moments are calculated

SURF features

SURF provide s us key points on which transformation takes place when compression of tampered image happens , features are extracted from these key points .

Block Intensity

We have taken 100 blocks but as an example we have

just shown 16 blocks for simplicity

Moments from DFT of DWT

For an image we have got 13 sub bands for which DFT is

calculated and moments are extracted .

SURF features

For an image SURF gives us important key points .

Integrated approach

We have trained SVMs on these features in different

combinations and tested on images with a GUI

giving us an option of choosing different

combinations and providing us results .

Result of our integrated approach

Findings and Conclusion:In rule based approach we find that exact match is too noisy and gives us less result whereas robust match and SURF with threshold give us similar accuracy but SURF is faster than robust match

In training based we found that the combination of Hu moments and the block intensity algorithm gives the highest efficiency .

We can conclude by saying that rule based is independent of any external parameters but is limited to only copy move forgery whereas training based approach encompasses the rigid transformations but the test result is only as good as the quality and quantity of the training images used.

Future Work For rule based approach , we need to get better

results where noise is concerned and also better

segmentation in SURF algorithm is needed .

In training based approach , more and better

quality images should be added to the training

database to make the result better.

THANK YOU