Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 25, Issue 10, Pages 4729-4742Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2016.2593583
Keywords
Copy-move forgery detection; fusion; behaviour knowledge space; multi-scale data analysis; multi-direction data analysis
Funding
- Brazilian National Council for Scientific and Technological Development [304472/2015-8, 477662/2013-7, 449638/2014-6, 304352/2012-8]
- Minas Gerais Research Foundation- FAPEMIG [APQ-00768-14]
- Brazilian Coordination for the Improvement of Higher Level Education Personnel - CAPES [99999.002341/2015-08]
- Microsoft Research
- Sao Paulo Research Foundation DejaVu Project [2015/19222-9]
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The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. Afterward, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Experimental results on complex data sets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature, show the effectiveness of the proposed method and its suitability for real-world applications.
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