Can we trust digital image forensics?
Proceedings of the 15th international conference on Multimedia
Detecting Video Forgeries Based on Noise Characteristics
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
A Survey of Passive Image Tampering Detection
IWDW '09 Proceedings of the 8th International Workshop on Digital Watermarking
Imaging sensor noise as digital X-ray for revealing forgeries
IH'07 Proceedings of the 9th international conference on Information hiding
A bibliography on blind methods for identifying image forgery
Image Communication
Detecting and extracting the photo composites using planar homography and graph cut
IEEE Transactions on Information Forensics and Security
Camera response functions for image forensics: an automatic algorithm for splicing detection
IEEE Transactions on Information Forensics and Security
Region duplication detection using image feature matching
IEEE Transactions on Information Forensics and Security
Detecting forgery from static-scene video based on inconsistency in noise level functions
IEEE Transactions on Information Forensics and Security
Analysis of multimedia forensic technique
Proceedings of the 2011 International Conference on Communication, Computing & Security
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
A theory of spherical harmonic identities for BRDF/Lighting transfer and image consistency
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Detecting doctored JPEG images via DCT coefficient analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
A forgery detection algorithm for exemplar-based inpainting images using multi-region relation
Image and Vision Computing
Accurate and robust localization of duplicated region in copy---move image forgery
Machine Vision and Applications
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The advance in image/video editing techniques has facilitated people in synthesizing realistic imageshideos that may hard to be distinguished from real ones by visual examination. This poses a problem: how to differentiate real imageshideosfrom doctored ones? This is a serious problembecause some legal issues may occur if there is no reliable way for doctored image/video detection when human inspection fails. Digital watermarking cannot solve this problem completely. Wepropose an approach that computes the response functions of the camera by selecting appropriate patches in different ways. An image may be doctored if the response functions are abnormal or inconsistent to each other. The normality of the response functions is classified by a trained support vector machine (SVM). Experiments show that our method is effective for high-contrast images with many textureless edges.