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Conference Papers Year : 2015

General-purpose image forensics using patch likelihood under image statistical models

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Abstract

This paper proposes a new, conceptually simple and effective forensic method to address both the generality and the fine-grained tampering localization problems of image forensics. Corresponding to each kind of image operation, a rich GMM (Gaussian Mixture Model) is learned as the image statistical model for small image patches. Thereafter, the binary classification problem, whether a given image block has been previously processed, can be solved by comparing the average patch log-likelihood values calculated on overlapping image patches under different GMMs of original and processed images. With comparisons to a powerful steganalytic feature, experimental results demonstrate the efficiency of the proposed method, for multiple image operations, on whole images and small blocks.
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Dates and versions

hal-01240755 , version 1 (09-12-2015)

Identifiers

  • HAL Id : hal-01240755 , version 1

Cite

Wei Fan, Kai Wang, François Cayre. General-purpose image forensics using patch likelihood under image statistical models. WIFS 2015 - 7th IEEE International Workshop on Information Forensics and Security (WIFS 2015), IEEE, Nov 2015, Rome, Italy. 6 p. ⟨hal-01240755⟩
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