Learning to Detect Fake Face Images in the Wild
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent methods have commonly applied deep learning to automatically extract salient and discriminative features to detect deepfakes.
“Recent meth- ods, e.g., [72, 73], have commonly applied deep learn- ing to automatically extract salient and discriminative features to detect deepfakes.”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [72]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 17 · Cited by 148
Research works printed but not linked: 12
- C.C.Hsu, et al., "Video forgery detection using correlation of noise residue." in Proc. of IEEE 10th Workshop on Multimedia Signal Processing, 2008.
- H. Farid, "Image forgery detection," IEEE Signal processing magazine 26.2, pp. 16-25, 2009.
- Karras, Tero, et al. "Progressive growing of GANS for improved quality, stability, and variation," arXiv preprint arXiv:1710.10196, 2017. · arXiv
- I. Goodfellow, et al. "Generative adversarial nets," in Proc. of Advances in neural information processing systems. 2014.
- A. Radford, et al.. "Unsupervised representation learning with deep convolutional generative adversarial networks," arXiv preprint arXiv:1511.06434, 2015. · arXiv
- M. Arjovsky, et al., "Wasserstein gan," arXiv preprint arXiv:1701.07875 (2017). · arXiv
- X. Mao, et al. "Least squares generative adversarial networks," 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.
- Y. LeCun, et al., "Handwritten digit recognition with a back-propagation network," Advances in neural information processing systems. 1990.
- I. Sutskever, et al. "On the importance of initialization and momentum in deep learning." International conference on machine learning. 2013.
- E. Simo-Serra, et al. "Discriminative learning of deep convolutional feature point descriptors," Computer Vision (ICCV), 2015 IEEE International Conference on. IEEE, 2015.
- 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.
- 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
- Authors
- Hsu, C.-C., Lee, C.-Y., & Zhuang, Y.-X.
- Affiliations
- National Pingtung University of Science and Technology (TW)
- National United University (TW)
- Venue
- 2018 International Symposium on Computer, Consumer and Control (IS3C)
- Publisher
- IEEE
- Published
- 2018-12
- Pages
- 388-391
- OpenAlex
- W2889677435
- Cited by
- 148
- References
- 17
- Citations by year
- 2026: 9 · 2025: 22 · 2024: 28 · 2023: 31 · 2022: 13 · 2021: 16 · 2020: 19 · 2019: 10
- FWCI
- 3.39
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Symposium on Computer, Consumer and Control (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Ministry of Science and Technology, Taiwan (MOST 107?)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, synthetic image detection, generative adversarial networks, contrastive learning, image tampering, binary classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
