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DefakeHop++: An Enhanced Lightweight Deepfake Detector
- Source :
- APSIPA Transactions on Signal and Information Processing. 11
- Publication Year :
- 2022
- Publisher :
- Now Publishers, 2022.
-
Abstract
- On the basis of DefakeHop, an enhanced lightweight Deepfake detector called DefakeHop++ is proposed in this work. The improvements lie in two areas. First, DefakeHop examines three facial regions (i.e., two eyes and mouth) while DefakeHop++ includes eight more landmarks for broader coverage. Second, for discriminant features selection, DefakeHop uses an unsupervised approach while DefakeHop++ adopts a more effective approach with supervision, called the Discriminant Feature Test (DFT). In DefakeHop++, rich spatial and spectral features are first derived from facial regions and landmarks automatically. Then, DFT is used to select a subset of discriminant features for classifier training. As compared with MobileNet v3 (a lightweight CNN model of 1.5M parameters targeting at mobile applications), DefakeHop++ has a model of 238K parameters, which is 16% of MobileNet v3. Furthermore, DefakeHop++ outperforms MobileNet v3 in Deepfake image detection performance in a weakly-supervised setting.
Details
- ISSN :
- 20487703
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- APSIPA Transactions on Signal and Information Processing
- Accession number :
- edsair.doi.dedup.....478550ef7a155d873daf5cecfa6a0a57