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Digital Image Forensics CS 365 By:- - Abhijit Sarang - Pankaj Jindal

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Digital Image Forensics. CS 365 By:- - Abhijit Sarang - Pankaj Jindal. Which of them are digitally manipulated?. How can we know?. - PowerPoint PPT Presentation

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Page 1: Digital Image Forensics

Digital Image Forensics

CS 365

By:-- Abhijit Sarang- Pankaj Jindal

Page 2: Digital Image Forensics

Which of them are digitally manipulated?

Page 3: Digital Image Forensics

How can we know?

• We call a digital image manipulated if either it has been retouched by a photo editing software or has been produced by the software itself.

• To prevent the former, the owner of the original image may introduce a watermark or a digital signature.

• But this process may not be feasible every time.

• Most approaches for detecting digital image manipulation are blind approaches.

Page 4: Digital Image Forensics

Our Methodology

• In [1], the authors argue that the statistical artifacts associated with images generated from cameras is inherently different form that associated with images manipulated by a software.

• These properties can be captured by analyzing the noise present in the image.

• Further, a discrete wavelet transform of the image can also be used to obtain some other statistical features .

Page 5: Digital Image Forensics

Building the feature vectors• Image De-noising

• Image was filtered using a wiener adaptive filter and a median filter.

• Neighborhood model of Wavelet sub bands

• To capture the strong correlation that exists between the wavelet subband coefficient, we find the residual error by building a neighborhood prediction model.

• Discrete wavelet transform

• We find the distance of the sub-bands distribution from the corresponding Gaussian distribution.

Page 6: Digital Image Forensics
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Results

• Image denoising

• True Positive = 37/47

• False Positive = 21/53

• Neighborhood model of Wavelet sub bands

• True Positive = 32/47

• False Positive = 11/53

• Discrete wavelet transform

• True Positive = 34/47

• False positive = 15/53

Page 9: Digital Image Forensics

Detecting Fake Regions

Detecting abnormal noise patterns in Image Detecting Duplicated Image Regions

Page 10: Digital Image Forensics
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References

Digital image Forensics For Identifying Computer Generated And Digital Camera Images Sintayehu Dehnie, Taha Sencar and Nasir Memon

Exposing Digital Forgeries by Detecting Duplicated Image Regions Alin C Popescu and Hany Farid

Noise Features for Image Tampering Detection and Steganalysis Hongmei Gou, Swaminathan, A., Min Wu

How realistic is photorealistic? Siwei Lyu and Hany Farid