Deep Fake Image Detection Based on Pairwise Learning
Source abstract
Abstract
Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learning-based approach for detecting the fake images by using the contrastive loss. First, several state-of-the-art GANs are employed to generate the fake–real image pairs. Next, the reduced DenseNet is developed to a two-streamed network structure to allow pairwise information as the input. Then, the proposed common fake feature network is trained using the pairwise learning to distinguish the features between the fake and real images. Finally, a classification layer is concatenated to the proposed common fake feature network to detect whether the input image is fake or real. The experimental results demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis · Advanced Image Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Management Information System, National Pingtung University of Science and Technology, 1, Shuefu Road, Neipu, Pingtung 91201, Taiwan
Department of Electrical Engineering, National United University, 2, Lienda, Miaoli 36063, Taiwan
Funding
Ministry of Science and Technology, Taiwan
References (29)
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1. Karras, T., Aila, T., Laine, S., and Lehtinen, J. (May, January 30). Progressive Growing of GANs for Improved Quality, Stability, and Variation. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada.
2. Brock, A., Donahue, J., and Simonyan, K. (2019, January 6–9). Large scale GAN training for high fidelity natural image synthesis. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA.
3. Zhu, J., Park, T., Isola, P., and Efros, A.A. (2017, January 22–29). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.244 ↗
4. (2019, March 30). AI Can Now Create Fake Porn, Making Revenge Porn Even More Complicated. Available online: https://theconversation.com/ai-can-now-create-fake-porn-making-revenge-porn-even-more-complicated-92267.
5. Hsu, C., Lee, C., and Zhuang, Y. (2018, January 6–8). Learning to detect fake face images in the Wild. Proceedings of the 2018 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan.10.1109/IS3C.2018.00104 ↗
6. Image authentication with tampering localization based on watermark embedding in wavelet domain10.1117/1.3127192 ↗
7. Hsu, C.C., Hung, T.Y., Lin, C.W., and Hsu, C.T. (2008, January 8–10). Video forgery detection using correlation of noise residue. Proceedings of the IEEE Workshop on Multimedia Signal Processing, Cairns, Australia.
8. Image forgery detection10.1109/MSP.2008.931079 ↗
9. Huaxiao Mo, B.C., and Luo, W. (2018, January 20–22). Fake Faces Identification via Convolutional Neural Network. Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, Innsbruck, Austria.
10. Dang, L., Hassan, S., Im, S., Lee, J., Lee, S., and Moon, H. (2018). Deep learning based computer generated face identification using convolutional neural network. Appl. Sci., 8.10.3390/app8122610 ↗
11. Marra, F., Gragnaniello, D., Cozzolino, D., and Verdoliva, L. (2018, January 10–12). Detection of GAN-Generated Fake Images over Social Networks. Proceedings of the IEEE Conference on Multimedia Information Processing and Retrieval, Miami, FL, USA.10.1109/MIPR.2018.00084 ↗
12. Chollet, F. (2017, January 21–26). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.195 ↗
13. Face image manipulation detection based on a convolutional neural network10.1016/j.eswa.2019.04.005 ↗
14. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8–13). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada.
15. Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv.
16. Arjovsky, M., Chintala, S., and Bottou, L. (2017, January 6–11). Wasserstein generative adversarial networks. Proceedings of the International Conference on Machine Learning, Sydney, Australia.
17. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A.C. (2017, January 4–9). Improved training of wasserstein gans. Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA.
18. Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., and Smolley, S.P. (2017, January 22–29). Least squares generative adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.304 ↗
19. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
20. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
21. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer.10.1007/978-3-319-46448-0_2 ↗
22. Chopra, S., Hadsell, R., and LeCun, Y. (2005, January 20–25). Learning a similarity metric discriminatively, with application to face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA.
23. LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., and Jackel, L.D. (1990, January 26–29). Handwritten digit recognition with a back-propagation network. Proceedings of the Advances in Neural Information Processing Systems, Denver, CO, USA.
24. Liu, Z., Luo, P., Wang, X., and Tang, X. (2015, January 7–13). Deep Learning Face Attributes in the Wild. Proceedings of the International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.425 ↗
25. Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013, January 16–21). On the importance of initialization and momentum in deep learning. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA.
26. Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2015, January 7–12). Is object localization for free?-weakly-supervised learning with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298668 ↗
27. Self-Attention generative adversarial networks
28. Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (May, January 30). Spectral normalization for generative adversarial networks. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada.
29. ImageNet large scale visual recognition challenge10.1007/s11263-015-0816-y ↗