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:
Participants: Baris Coskun Taha Sencar Nasir Memon Sevinc Bayram Yagiz Sutcu Kurt Rosenfeld
Resources:
ISIS in the NEWS
- ISIS Media Forensics Research Featured in Thomson Security Newsletter, July 1, 2009
- Reasearchers Link Digital Photographs to Source Camera PursuitMagazine,
Journal of Professional Excellence for Investigators, 24 November 2008
- Digital Photos Give Away a Camera's Make and Model Slashdotted!!,
17 November 2008
- Digital images contain their maker's mark
NewScientist Magazine, Issue 2682, Page 30, 14 November 2008
- Finding the truth behind photographs SPIE
Newsroom, 3 May 2006
- Photo Chop Shop Technology Magazine, published
by MIT Review, 06 December 2005
Data Sets and Codes
Book Chapter
Journal
- S. Bayram, H. T. Sencar, and N. Memon, Classification of digital camera-models based on
demosaicing artifacts, Digital Investigation, Volume 5,
Issues 1-2, September 2008, Pages 49-59. [BibTex]
- 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] , [Code]
, [Images]
Conference
- K. Rosenfeld and H. T. Sencar, A Study of
The Robustness of PRNU-based Camera Identification, Proc. SPIE,
Vol. 7254, 72540M, 2009.
- A. E. Dirik and N. Memon, Image Tamper Detection Based on Demosaicing Artifacts,
IEEE ICIP 09, November 2009, Cairo Egypt. [BibTex] [Images]
- Y. Fang, A. E. Dirik, X. Sun, and N. Memon, Source Class Identification for DSLR and Compact Camera
s, IEEE International Workshop on Multimedia Signal Processing - MMSP
09, October, 2009. [BibTex]
- A. E. Dirik, H. T. Sencar, and N. Memon, Flatbed Scanner Identification Based On Dust and Scratches
Over Scanner Platen, IEEE International Conference on
Acoustics, Speech and Signal Processing, ICASSP 2009, Taipei Taiwan. [BibTex]
- S. Bayram, H. T. Sencar, and N. Memon, An Efficient and Robust Method For Detecting Copy-Move
Forgery, IEEE International Conference on Acoustics, Speech
and Signal Processing, ICASSP 2009, Taipei Taiwan. [BibTex]
- S. Bayram, H. T. Sencar, and N. Memon, A Survey of Copy-Move Forgery Detection Techniques, IEEE
Western New York Image Processing Workshop, September 2008, NY (Best
Student Paper Award). [BibTex]
- Y. Sutcu, B. Coskun, H. T. Sencar, and N. Memon, Tamper detection based on regularity of wavelet transform
coefficients, Proc. of IEEE ICIP, 2007. [BibTex]
- A. E. Dirik, S. Bayram, H. T. Sencar, and N. Memon, New features to identify computer generated images,
Proc. of IEEE ICIP, 2007. [BibTex] , [Code]
- Y. Sutcu, S. Bayram, H. T. Sencar, and N. Memon, Improvements on sensor noise based source camera
identification, Proc. of IEEE ICME, 2007. [BibTex]
- A. E. Dirik, H. T. Sencar, and N. Memon, Source camera identification based on sensor dust
characteristics , Proc. of IEEE SAFE, 2007. [BibTex]
- 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. [BibTex]
- 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. [BibTex]
- O. Celiktutan, I. Avcibas, B. Sankur, and N. Memon, Source cell-phone identification, Proc. of
ADCOM, 2006. [ BibTex]
- 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. [BibTex]
- S. Bayram, H. T. Sencar, N. Memon, and I. Avcibas, Source camera identification based on CFA interpolation,
Proc. of IEEE ICIP, 2005. [BibTex]
- M. Kharrazi, H. T. Sencar, and N. Memon, Blind source camera identification, Proc. of
IEEE ICIP, 2004. [BibTex]
- I. Avcibas, S. Bayram, N. Memon, M. Ramkumar, and B.
Sankur, A classifier design for detecting image manipulation,
Proc. of IEEE ICIP, 2004. [BibTex]
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