In the analog world, an image (specifically a photograph) has generally been accepted as a “proof of occurrence” of the event it depicts. In today’s digital age, however, the creation and manipulation of digital images and videos are made simple through digital processing tools that are easily and widely available. As a consequence, we can no longer take the authenticity of images and videos for granted, be they analog or digital. This is especially true when it comes to legal evidence. Image and video forensics, in this context, is concerned with uncovering some underlying fact about an image or video.
The past few years have seen a growth of research on image forensics. The work has come to focus mainly on three types of problems:
- Image origin/type identification. The goal is to determine through what means a given digital image was originally generated, e.g., digital camera, scanner, computer graphics software, etc. The most immediate challenge in this area is to discriminate computer generated images, which do not depict real-life occurrences, from real images.
- Image source identification. Given the type of an image, this research area aims at identifying the class and/or individual characterisctics of the mechanism that generated the digital image. This essentially entails associating the image with a class of sources that have common characteristics (e.g., device model) and matching the image to an individual source device.
- Image forgery detection. In this field of research, the objective is to determine whether a given digital image has undergone any form of modification or processing after it was initially captured. Determining the processing history of an image and identification of tampered image parts are the foremost research goals.
Research at ISIS has developed many techniques that address the three dimensions of the digital image forensics.
Sponsors:
- AFOSR
- NIJ
Participants:
Baris Coskun
Taha Sencar
Nasir Memon
Emir Dirik
Sevinc Bayram
Yagiz Sutcu
Kurt Rosenfeld
Resources:
- A. E. Dirik, H. T. Sencar, and N. Memon, Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust, IEEE Trans. on Information Forensics and Security, Sept., 2008. [BibTex]
- H. T. Sencar, and N. Memon, Overview of State-of-the-art in Digital Image Forensics, Part of Indian Statistical Institute Platinum Jubilee Monograph series titled ‘Statistical Science and Interdisciplinary Research,’ World Scientific Press, (expected publication date) 2008.
- Y. Sutcu, B. Coskun, H. T. Sencar, and N. Memon, Tamper detection based on regularity of wavelet transform coefficients, Proc. of IEEE ICIP, 2007
- A. E. Dirik, S. Bayram, H. T. Sencar, and N. Memon, New features to identify computer generated images, Proc. of IEEE ICIP, 2007 [BibTex]
- Y. Sutcu, S. Bayram, H. T. Sencar, and N. Memon, Improvements on sensor noise based source camera identification, Proc. of IEEE ICME, 2007
- A. E. Dirik, H. T. Sencar, and N. Memon, Source camera identification based on sensor dust characteristics , Proc. of IEEE SAFE, 2007 [BibTex]
- S. Bayram, I. Avcibas, B. Sankur and N. Memon, Image manipulation detection Journal of Electronic Imaging, 15(4) 041102, October-December 2006
- S. Dehnie, H. T. Sencar and N. Memon, Identification of computer generated and digital camera images for digital image forensics, Proc. of IEEE ICIP, 2006
- S. Bayram, H. T. Sencar, and N. Memon Improvements on source camera-model identification based on CFA interpolation,Proc. of WG 11.9 International Conference on Digital Forensics, 2006
- O. Celiktutan, I. Avcibas, B. Sankur, and N. Memon, Source cell-phone identification,Proc. of ADCOM, 2005
- S. Bayram, I. Avcibas, B. Sankur, N. Memon, Image Manipulation Detection with Binary Similarity Measures, 13th European Signal Processing Conference, Vol. I, 752-755, Antalya-TURKEY, 2005
- S. Bayram, H. T. Sencar, N. Memon, and I. Avcibas, Source camera identification based on CFA interpolation,Proc. of IEEE ICIP, 2005
- M. Kharrazi, H. T. Sencar, and N. Memon, Blind source camera identification,Proc. of IEEE ICIP, 2004
- I. Avcibas, S. Bayram, N. Memon, M. Ramkumar, and B. Sankur, A classifier design for detecting image manipulation,Proc. of IEEE ICIP, 2004
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