Detecting Digital Forgeries Using Bispectral Analysis
Detecting Digital Forgeries Using Bispectral Analysis
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Detecting image near-duplicate by stochastic attributed relational graph matching with learning
Proceedings of the 12th annual ACM international conference on Multimedia
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Do These News Videos Portray a News Event from Different Ideological Perspectives?
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Detecting doctored JPEG images via DCT coefficient analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Scalable detection of partial near-duplicate videos by visual-temporal consistency
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Automatic video archaeology: tracing your online videos
Proceedings of second ACM SIGMM workshop on Social media
Visual memes in social media: tracking real-world news in YouTube videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Video hyperlinking: libraries and tools for threading and visualizing large video collection
Proceedings of the 20th ACM international conference on Multimedia
Video archaeology: understanding video manipulation history
Multimedia Tools and Applications
Exploring heuristic and optimum branching algorithms for image phylogeny
Journal of Visual Communication and Image Representation
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We propose a system for automatically detecting the ways in which images have been copied and edited or manipulated. We draw upon these manipulation cues to construct probable parent-child relationships between pairs of images, where the child image was derived through a series of visual manipulations on the parent image. Through the detection of these relationships across a plurality of images, we can construct a history of the image, called the visual migration map (VMM), which traces the manipulations applied to the image through past generations. We propose to apply VMMs as part of a larger internet image archaeology system (IIAS), which can process a given set of related images and surface many interesting instances of images from within the set. In particular, the image closest to the "original" photograph might be among the images with the most descendants in the VMM. Or, the images that are most deeply descended from the original may exhibit unique differences and changes in the perspective being conveyed by the author. We evaluate the system across a set of photographs crawled from the web and find that many types of image manipulations can be automatically detected and used to construct plausible VMMs. These maps can then be successfully mined to find interesting instances of images and to suppress uninteresting or redundant ones, leading to a better understanding of how images are used over different times, sources, and contexts.