Learning to detect fake face images in the wild

Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018

Volumn , Issue , 2018, Pages 388-391

  Hsu, Chih Chung ^a   Lee, Chia Yen ^b   Zhuang, Yi Xiu ^a  

^a NATIONAL PINGTUNG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL UNITED UNIVERSITY   (Taiwan)

Author keywords

Contrastive loss; Deep learning; Forgery detection; Fully convolutional network; GAN

Indexed keywords

COMPUTER VISION; DEEP LEARNING; FAKE DETECTION;

BINARY CLASSIFIERS; COMPUTER-GENERATED IMAGES; CONTRASTIVE LOSS; CONVOLUTIONAL NETWORKS; DEEP LEARNING; FACE IMAGES; FORGERY DETECTIONS; FULLY CONVOLUTIONAL NETWORK; PERSONAL SAFETY; REALISTIC IMAGES;

GENERATIVE ADVERSARIAL NETWORKS;

EID: 85063196021     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IS3C.2018.00104     Document Type: Conference Paper

References (14)

1. AI can now create fake porn, making revenge porn even more complicated, http: //theconversation. com/ai-can-now-create-fake-pornmaking-revenge-porn-even-more-complicated-92267, The conversation media group, 2018.
2. C. C. Hsu, et al., "Video forgery detection using correlation of noise residue. " in Proc. of IEEE 10th Workshop on Multimedia Signal Processing, 2008.
3. H. Farid, "Image forgery detection, " IEEE Signal processing magazine 26. 2, pp. 16-25, 2009.
4. Karras, Tero, et al. "Progressive growing of GANS for improved quality, stability, and variation, " arXiv preprint arXiv: 1710. 10196, 2017.
5. I. Goodfellow, et al. "Generative adversarial nets, " in Proc. of Advances in neural information processing systems. 2014.
6. A. Radford, et al. "Unsupervised representation learning with deep convolutional generative adversarial networks, " arXiv preprint arXiv: 1511. 06434, 2015.
7. M. Arjovsky, et al., "Wasserstein gan, " arXiv preprint arXiv: 1701. 07875 (2017).
8. Gulrajani, Ishaan, et al. "Improved training of wasserstein gans, " Advances in Neural Information Processing Systems. 2017.
9. X. Mao, et al. "Least squares generative adversarial networks, " 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.
10. Y. LeCun, et al., "Handwritten digit recognition with a back-propagation network, " Advances in neural information processing systems. 1990.
11. I. Sutskever, et al. "On the importance of initialization and momentum in deep learning. " International conference on machine learning. 2013.
12. E. Simo-Serra, et al. "Discriminative learning of deep convolutional feature point descriptors, " Computer Vision (ICCV), 2015 IEEE International Conference on. IEEE, 2015.
13. Y. L. Chen and C. T. Hsu. "Detecting doubly compressed images based on quantization noise model and image restoration, " in Proc. of IEEE International Workshop on. Multimedia Signal Processing, Oct. 2009.
14. J. Long, et al. "Fully convolutional networks for semantic segmentation, " in Proc. of the IEEE conference on computer vision and pattern recognition, pp. 640-651, May 2016