Detecting Computer Generated Images with Deep Convolutional Neural Networks
| Autor: | Tiago Carvalho, Guilherme C. S. Ruppert, Edmar Rezende |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science Computer-generated imagery Feature extraction 020206 networking & telecommunications 02 engineering and technology Convolutional neural network Image (mathematics) Computer graphics 0202 electrical engineering electronic engineering information engineering Selection (linguistics) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Transfer of learning |
| Zdroj: | SIBGRAPI |
| DOI: | 10.1109/sibgrapi.2017.16 |
| Popis: | Computer graphics techniques for image generation are living an era where, day after day, the quality of produced content is impressing even the more skeptical viewer. Although it is a great advance for industries like games and movies, it can become a real problem when the application of such techniques is applied for the production of fake images. In this paper we propose a new approach for computer generated images detection using a deep convolutional neural network model based on ResNet-50 and transfer learning concepts. Unlike the state-of-the-art approaches, the proposed method is able to classify images between computer generated or photo generated directly from the raw image data with no need for any pre-processing or hand-crafted feature extraction whatsoever. Experiments on a public dataset comprising 9700 images show an accuracy higher than 94%, which is comparable to the literature reported results, without the drawback of laborious and manual step of specialized features extraction and selection. |
| Databáze: | OpenAIRE |
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