1 / 11

Digital Image Forensics

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

celeste-marty
Download Presentation

Digital Image Forensics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. Which of them are digitally manipulated?

  3. 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.

  4. 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 .

  5. 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.

  6. 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

  7. Detecting Fake Regions • Detecting abnormal noise patterns in Image • Detecting Duplicated Image Regions

  8. 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

More Related