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Image Splicing Detection using Camera Response Function Consistency and Automatic Segmentation

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2 Author(s)
Yu-Feng Hsu ; Columbia Univ., New York ; Shih-Fu Chang

We propose a fully automatic spliced image detection method based on consistency checking of camera characteristics among different areas in an image. A test image is first segmented into distinct areas. One camera response function (CRF) is estimated from each area using geometric invariants from locally planar irradiance points (LPIPs). To classify a boundary segment between two areas as authentic or spliced, CRF cross fitting scores and area intensity features are computed and fed to SVM-based classifiers. Such segment-level scores are further fused to form the image-level decision. Tests on both the benchmark data set and an unseen high-quality spliced data set reach promising performance levels with 70% precision and 70% recall.

Published in:

Multimedia and Expo, 2007 IEEE International Conference on

Date of Conference:

2-5 July 2007

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