Forgery & Forensics

1. Forgery & Forensics Hany Farid ACM Proceedings of the 8th Workshop onMultimedia and Security, Sep. 2006

2. Image Tampering • Digital Tampering: • Compositing. • Morphing. • Re-touching. • Enhancing. • Computer graphics. • Painted.

3. Forensics • Forensic science, the application of a broad spectrum of sciences to answer questions of interest to the legal system. • Digital forensics, the application of the scientific method to digital media in order to establish factual information for judicial review.

4. Discussion • The problem of detecting digital forgeries is a complex one with no universally applicable solution. • Reliable forgery detection should be approached from multiple directions.

5. Exposing Digital Forgeries in Scientific Images • In at least one journal, it is estimated that as many as 20% of accepted manuscripts contain figures with inappropriate manipulations, and 1% with fraudulent manipulations.

6. Image Manipulation • Action of each manipulation scheme: • Deletion, (a). • A band was erased. • Healing, (b). • Several bands were removing using Photoshop’s “healing brush.” • Duplication, (c). • A band was copied and pasted into a new location.

7. Image Manipulation • Effect of each manipulation scheme: • Deletion. • Remove small amounts of noise that are present through the dark background of the image. • Healing. • Disturb the underlying spatial frequency (texture). • Duplication. • Leave behind an obvious statistical pattern – two regions in the image are identical. • Formulate the problem of detecting each of these statistical patterns as an image segmentation problem.

8. Image Segmentation: Texture • For healing. • Ig (．): the magnitude of the image gradient at a given pixel.

9. Image Segmentation: Texture • s • d (．): 1D deravative filter. • [0.0187 0.1253 0.1930 0.0 −0.1930 −0.1253 −0.0187] • p (．): low-pass filter. • [0.0047 0.0693 0.2454 0.3611 0.2454 0.0693 0.0047]

10. Automatic Detection