A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of Linear and Cubic Interpolation in JPEG Compressed Images
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
Exposing digital forgeries from JPEG ghosts
IEEE Transactions on Information Forensics and Security
A Markov process based approach to effective attacking JPEG steganography
IH'06 Proceedings of the 8th international conference on Information hiding
Statistical tools for digital forensics
IH'04 Proceedings of the 6th international conference on Information Hiding
Exposing digital forgeries by detecting traces of resampling
IEEE Transactions on Signal Processing
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Steganalysis for Markov cover data with applications to images
IEEE Transactions on Information Forensics and Security
Blind Authentication Using Periodic Properties of Interpolation
IEEE Transactions on Information Forensics and Security
A bibliography on blind methods for identifying image forgery
Image Communication
Digital image forensics: a booklet for beginners
Multimedia Tools and Applications
Seam carving estimation using forensic hash
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
Shift recompression-based feature mining for detecting content-aware scaled forgery in JPEG images
Proceedings of the Twelfth International Workshop on Multimedia Data Mining
A patch analysis approach for seam-carved image detection
ACM SIGGRAPH 2013 Posters
A patch analysis method to detect seam carved images
Pattern Recognition Letters
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"Seam carving" is a recently introduced content aware image resizing algorithm. This method can also be used for image tampering. In this paper, we explore techniques to detect seam carving (or seam insertion) without knowledge of the original image. We employ a machine learning based framework to distinguish between seam-carved (or seam-inserted) and normal images. It is seen that the 324-dimensional Markov feature, consisting of 2D difference histograms in the block-based Discrete Cosine Transform domain, is well-suited for the classification task. The feature yields a detection accuracy of 80% and 85% for seam carving and seam insertion, respectively. For seam insertion, each new pixel that is introduced is a linear combination of its neighboring pixels. We detect seam insertions based on this linear relation, with a high detection accuracy of 94% even for very low seam insertion rates. We show that the Markov feature is also useful for scaling and rotation detection.