Behavior Knowledge Space-Based Fusion for Copy–Move Forgery Detection
Autor: | Jefersson A. dos Santos, Anderson Rocha, Carlos Alfaro, John E. Vargas-Munoz, Anselmo Ferreira, Pablo Fonseca, Siovani Cintra Felipussi |
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Rok vydání: | 2016 |
Předmět: |
021110 strategic
defence & security studies Training set Knowledge space business.industry Computer science Feature extraction 0211 other engineering and technologies Conditional probability 02 engineering and technology Machine learning computer.software_genre Computer Graphics and Computer-Aided Design Text mining Gamut Robustness (computer science) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data mining business computer Software |
Zdroj: | IEEE Transactions on Image Processing. 25:4729-4742 |
ISSN: | 1941-0042 1057-7149 |
DOI: | 10.1109/tip.2016.2593583 |
Popis: | 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. |
Databáze: | OpenAIRE |
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