Copy-Move Forgery Detection (CMFD) Using Deep Learning for Image and Video Forensics
| Autor: | Yohanna Rodríguez-Ortega, M L Dora Ballesteros, Diego Renza |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Generalization copy-move forgery detection Data classification Inference Image processing 02 engineering and technology transfer learning Machine learning computer.software_genre lcsh:Computer applications to medicine. Medical informatics Article computer vision lcsh:QA75.5-76.95 Video editing 0202 electrical engineering electronic engineering information engineering Radiology Nuclear Medicine and imaging lcsh:Photography Electrical and Electronic Engineering business.industry Deep learning deep learning 020207 software engineering VGG fake image lcsh:TR1-1050 Computer Graphics and Computer-Aided Design lcsh:R858-859.7 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence lcsh:Electronic computers. Computer science Transfer of learning F1 score business computer |
| Zdroj: | Journal of Imaging, Vol 7, Iss 59, p 59 (2021) Journal of Imaging Volume 7 Issue 3 |
| Popis: | With the exponential growth of high-quality fake images in social networks and media, it is necessary to develop recognition algorithms for this type of content. One of the most common types of image and video editing consists of duplicating areas of the image, known as the copy-move technique. Traditional image processing approaches manually look for patterns related to the duplicated content, limiting their use in mass data classification. In contrast, approaches based on deep learning have shown better performance and promising results, but they present generalization problems with a high dependence on training data and the need for appropriate selection of hyperparameters. To overcome this, we propose two approaches that use deep learning, a model by a custom architecture and a model by transfer learning. In each case, the impact of the depth of the network is analyzed in terms of precision (P), recall (R) and F1 score. Additionally, the problem of generalization is addressed with images from eight different open access datasets. Finally, the models are compared in terms of evaluation metrics, and training and inference times. The model by transfer learning of VGG-16 achieves metrics about 10% higher than the model by a custom architecture, however, it requires approximately twice as much inference time as the latter. |
| Databáze: | OpenAIRE |
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