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Learning to Detect Fake Face Images in the Wild
- Publication Year :
- 2018
- Publisher :
- arXiv, 2018.
-
Abstract
- Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns. For example, GAN can be used to generate a tampered video for specific people and inappropriate events, creating images that are detrimental to a particular person, and may even affect that personal safety. In this paper, we will develop a deep forgery discriminator (DeepFD) to efficiently and effectively detect the computer-generated images. Directly learning a binary classifier is relatively tricky since it is hard to find the common discriminative features for judging the fake images generated from different GANs. To address this shortcoming, we adopt contrastive loss in seeking the typical features of the synthesized images generated by different GANs and follow by concatenating a classifier to detect such computer-generated images. Experimental results demonstrate that the proposed DeepFD successfully detected 94.7% fake images generated by several state-of-the-art GANs.<br />Comment: 4 pages to appear in IEEE IS3C Conference (IEEE International Symposium on Computer, Consumer and Control Conference), Dec. 2018
- Subjects :
- FOS: Computer and information sciences
Contextual image classification
Computer science
business.industry
Deep learning
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
020206 networking & telecommunications
Pattern recognition
02 engineering and technology
010501 environmental sciences
01 natural sciences
Facial recognition system
Multimedia (cs.MM)
Discriminative model
Binary classification
0202 electrical engineering, electronic engineering, information engineering
Artificial intelligence
business
Classifier (UML)
Digital watermarking
Computer Science - Multimedia
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....160879dcf96c7bf25fac8806b08edcc2
- Full Text :
- https://doi.org/10.48550/arxiv.1809.08754