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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Computer generated images vs. digital photographs: A synergetic feature and classifier combination approach

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Autor(es):
Tokuda, Eric [1] ; Pedrini, Helio [1] ; Rocha, Anderson [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, UNICAMP, Inst Comp, BR-13083852 Campinas, SP - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION; v. 24, n. 8, p. 1276-1292, NOV 2013.
Citações Web of Science: 10
Resumo

The development of powerful and low-cost hardware devices allied with great advances on content editing and authoring tools have promoted the creation of computer generated images (CG) to a degree of unrivaled realism. Differentiating a photo-realistic computer generated image from a real photograph (PG) can be a difficult task to naked eyes. Digital forensics techniques can play a significant role in this task. As a matter of fact, important research has been made by the scientific community in this regard. Most of the approaches focus on single image features aiming at detecting differences between real and computer generated images. However, with the current technology advances, there is no universal image characterization technique that completely solves this problem. In our work, we (1) present a complete study of several CG versus PG approaches; (2) create a large and heterogeneous dataset to be used as a training and validation database; (3) implement representative methods of the literature; and (4) devise automatic ways for combining the best approaches. We compared the implemented methods using the same validation environment showing their pros and cons with a common benchmark protocol. We collected approximately 4850 photographs and 4850 CGs with large diversity of image content and quality. We implemented a total of 13 methods. Results show that this set of methods can achieve up to 93% of accuracy when used without any form of machine learning fusion. The same methods, when combined through the implemented fusion schemes, can achieve an accuracy rate of 97%, representing a reduction of 57% of the classification error over the best individual result. (C) 2013 Elsevier Inc. All rights reserved. (AU)

Processo FAPESP: 11/22749-8 - Desafios em visualização exploratória de dados multidimensionais: novos paradigmas, escalabilidade e aplicações
Beneficiário:Luis Gustavo Nonato
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 10/13745-6 - Projeto e desenvolvimento de técnicas forenses para identificação de imagens sintéticas
Beneficiário:Eric Keiji Tokuda
Linha de fomento: Bolsas no Brasil - Mestrado
Processo FAPESP: 10/05647-4 - Computação forense e criminalística de documentos: coleta, organização, classificação e análise de evidências
Beneficiário:Anderson de Rezende Rocha
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores