Welcome to TrustFoto Homepage
Summary | People | Individual Projects | Publications | Download | Demo | More Information | Sponsor | Internal Links
Summary (Project overview slides)
Trustworthy photographs play an important role in many applications such as news reporting, intelligence information gathering, criminal investigation, security surveillance, as well as health care. However, with the advent of digital age, the trustworthiness of pictures could no longer be taken for granted. This project will develop a completely blind and passive system for detecting digital photograph tampering. No extra encryption, signature extraction, or information embedding processes are needed. Content tampering operations are detected at the point of checking by analyzing the natural signal/scene characteristics in the image.
We take an innovative approach integrating techniques from signal processing and computer graphics. The signal processing method involves effective use of higher-order signal statistics, signal acquisition device modeling, image decomposition, and image structural analysis to identify tampering artifacts at the signal level. The computer graphics approach includes novel techniques of 3D geometry estimation, illumination field recovery, and scene reconstruction to detect inconsistency at the scene level like shadows, shading, and geometry.
We aim at a successful system that makes any attacking maneuver as difficult as possible. The system will also provide equal emphasis on robustness and informativeness – suspicions will be explained with locations and reasons. To achieve broader impacts, the proposed research will include deployment of a public image forgery detection engine, release of a large original data set, and definitions of evaluation benchmark.
Prof. Shih-Fu Chang (PI)
Prof. Ravi Ramamoorthi (co-PI)
- Tian-Tsong Ng, Shih-Fu Chang, and Mao-Pei Tsui. Using Geometry Invariants For Camera Response Function Estimation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, June 2007. [pdf]
- Yu-Feng Hsu, Shih-Fu Chang. Image Splicing Detection Using Camera Response Function Consistency And Automatic Segmentation. In Interational Conference on Multimedia and Expo (ICME), Beijing, China, July 2007. [pdf]
- T.-T. Ng, S.-F. Chang, and M.-P. Tsui. Lessons Learned from Online Classification of Photo-Realistic Computer Graphics and Photographs. IEEE Workshop on Signal Processing Applications for Public Security and Forensics (SAFE), Washington DC, April 2007. [pdf][slides]
- Yu-Feng Hsu, Shih-Fu Chang. Detecting Image Splicing Using Geometry Invariants And Camera Characteristics Consistency. In Interational Conference on Multimedia and Expo (ICME), Toronto, Canada, July 2006. [Abstract] [pdf][slides]
- Tian-Tsong Ng, Shih-Fu Chang. An Online System for Classifying Computer Graphics Images from Natural Photographs. In SPIE Electronic Imaging, San Jose, CA, January 2006. [Abstract] [pdf] [slides]
- Tian-Tsong Ng, Shih-Fu Chang, Jessie Hsu, Lexing Xie, Mao-Pei Tsui. Physics-Motivated Features for Distinguishing Photographic Images and Computer Graphics. In ACM Multimedia, Singapore, November 2005. [Abstract] [pdf] [slides]
- Tian-Tsong Ng, Shih-Fu Chang. A Model for Image Splicing. In IEEE International Conference on Image Processing (ICIP), Singapore, October 2004. [Abstract] [pdf] [slides]
- Tian-Tsong Ng, Shih-Fu Chang, Qibin Sun. Blind Detection of Photomontage Using Higher Order Statistics. In IEEE International Symposium on Circuits and Systems (ISCAS), Vancouver, Canada, May 2004. [Abstract] [pdf] [slides]
Tian-Tsong Ng, Shih-Fu Chang, Ching-Yung Lin, Qibin Sun. Passive-blind Image Forensics. In Multimedia Security Technologies for Digital Rights, W. Zeng, H. Yu, and Ching-Yung Lin (eds.), Elsvier, 2006. [Abstract] [pdf]
- Project overview slides
- Tian-Tsong Ng, Shih-Fu Chang, Mao-Pei Tsui. Camera Response Function Estimation from a Single-channel Image Using Differential Invariants. ADVENT Technical Report #216-2006-2 Columbia University, March 2006. [Abstract] [pdf]
- Tian-Tsong Ng, Shih-Fu Chang. Classifying Photographic and Photorealistic Computer Graphic Images using Natural Image Statistics. ADVENT Technical Report #220-2006-6 Columbia University, Oct 2004. [Abstract] [pdf]
- Tian-Tsong Ng, Shih-Fu Chang, Jessie Hsu, Martin Pepeljugoski. Columbia Photographic Images and Photorealistic Computer Graphics Dataset. ADVENT Technical Report #205-2004-5 Columbia University, February 2005. [Abstract] [pdf]
- Tian-Tsong Ng, Shih-Fu Chang. Blind Detection of Digital Photomontage using Higher Order Statistics. ADVENT Technical Report #201-2004-1 Columbia University, June 2004. [Abstract] [pdf]
- Tian-Tsong Ng, Shih-Fu Chang. A Data Set of Authentic and Spliced Image Blocks. ADVENT Technical Report #203-2004-3 Columbia University, June 2004. [Abstract] [pdf]
- Columbia Image Splicing Detection Evaluation Dataset
- Columbia Uncompressed Image Splicing Detection Evaluation Dataset
- Columbia Photographic Images and Photorealistic Computer Graphics Dataset
Online demo for the Photographic Image vs. Photorealistic CG Classification
Links to More Information
- Columbia University Digital Video and Multimedia Lab
- Columbia University Graphics Lab
National Science Foudantion Cyber Trust Program, Award No IIS-04-30258.
TrustFoto Twiki site (password restricted)