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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Abstract-Porous media modeling is relevant in several applications, such as agricultural engineering, where soil compaction analysis requires the estimation of soil transport properties. For example, the prediction of root growing patterns and their environmental impact is usually measured by analyzing soil fluid infiltration capacity and water retention. Recently, tomographic images have been used in nondestructive tests of soil. However, using such images is challenging for two reasons: 1) Tomographic images are usually noisy, which complicates their segmentation, and 2) modeling the soil structure requires establishing adjacency relations among neighboring tomographic slices, which has a significant computational cost due to the combinatorial nature of this problem. In this paper, we propose a solution for both problems. The experimental results show that soil samples can be analyzed and classified with significant accuracy using our proposed approach.

O conteúdo deste livro está licenciado sob a Licença de Atribuição Creative Commons 4.0. Com ela é permitido compartilhar o livro, devendo ser dado o devido crédito, não podendo ser utilizado para fins comerciais e nem ser alterada.

O conteúdo deste livro está licenciado sob a Licença de Atribuição Creative Commons 4.0. Com ela é permitido compartilhar o livro, devendo ser dado o devido crédito, não podendo ser utilizado para fins comerciais e nem ser alterada.