Incremental learning for the detection and classification of GAN-generated images
Open accessAbstract
Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images, hardly distinguishable from real ones. In particular, human face generation achieved a stunning level of realism, opening new opportunities for the creative industry but, at the same time, new scary scenarios where such content can be maliciously misused. Therefore, it is essential to develop innovative methodologies to automatically tell apart real from computer generated multimedia, possibly able to follow the evolution and continuous improvement of data in terms of quality and realism. In the last few years, several deep learning-based solutions have been proposed for this problem, mostly based on Convolutional Neural Networks (CNNs). Although results are good in controlled conditions, it is not clear how such proposals can adapt to real-world scenarios, where learning needs to continuously evolve as new types of generated data appear. In this work, we tackle this problem by proposing an approach based on incremental learning for the detection and classification of GAN-generated images. Experiments on a dataset comprising images generated by several GAN-based architectures show that the proposed method is able to correctly perform discrimination when new GANs are presented to the network.
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Deepfake detection has so far been approached by passive techniques that operate after the fact, i.e., after the forged content has been created, distributed and potentially manipulated.
“Deepfake detection has been approached so far by means of passive techniques [185,186,187] that operate after the fact, meaning they are applied after the forged content has been created, distributed, and potentially manipulated.”
From Deepfake Media Forensics: Status and Future Challenges (2025) · cites this paper as [187]
The Introduction (page unavailable) describes methods that inspect images to determine whether they are GAN-generated, including detection after compression. This supports detection after creation and some subsequent processing, but the paper does not establish that detection occurs only after distribution or that all deepfake-detection techniques to date are passive. (LLM, conf=0.83)
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Cites
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1611835114 · Cited by 7,899 · see citing works
StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00916 · Cited by 4,163 · see citing works
iCaRL: Incremental Classifier and Representation Learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., & Lampert, C. H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.587 · Cited by 3,988 · see citing works
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717 · see citing works
Gradient Episodic Memory for Continual Learning
Lopez-Paz, D., & Ranzato, M. (2017) · arXiv · doi:10.48550/arxiv.1706.08840 · Cited by 1,403 · see citing works
BEGAN: Boundary Equilibrium Generative Adversarial NetworksPreprint
Berthelot, D., Schumm, T., & Metz, L. (2017) · arXiv.org · doi:10.48550/arxiv.1703.10717 · Cited by 1,217 · see citing works
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations
Matern, F., Riess, C., & Stamminger, M. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00020 · Cited by 824 · see citing works
Detection of GAN-Generated Fake Images over Social Networks
Marra, F., Gragnaniello, D., Cozzolino, D., & Verdoliva, L. (2018) · 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2018.00084 · Cited by 399 · see citing works
Self-citationDo GANs leave artificial fingerprints?
Marra, F., Gragnaniello, D., Verdoliva, L., & Poggi, G. (2019) · 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr.2019.00103 · Cited by 361 · see citing works
Self-citationDetecting GAN generated Fake Images using Co-occurrence Matrices
Nataraj, L., Mohammed, T. M., Manjunath, B. S., Chandrasekaran, S., Flenner, A., Bappy, J. H., & Roy-Chowdhury, A. K. (2019) · Electronic Imaging · doi:10.2352/issn.2470-1173.2019.5.mwsf-532 · Cited by 331 · see citing works
Identification of deep network generated images using disparities in color components
Li, H., Li, B., Tan, S., & Huang, J. (2020) · Signal Processing · doi:10.1016/j.sigpro.2020.107616 · Cited by 245 · see citing works
ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection
Cozzolino, D., Thies, J., Rössler, A., Riess, C., Nießner, M., & Verdoliva, L. (2018) · arXiv · doi:10.48550/arxiv.1812.02510 · Cited by 221 · see citing works
Self-citationOn the Generalization of GAN Image Forensics
Xuan, X., Peng, B., Wang, W., & Dong, J. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-31456-9_15 · Cited by 184 · see citing works
Exposing GAN-synthesized Faces Using Landmark Locations
Yang, X., Li, Y., Qi, H., & Lyu, S. (2019) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3335203.3335724 · Cited by 127 · see citing works
Detecting GAN-generated Imagery using Color CuesPreprint
McCloskey, S., & Albright, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.08247 · Cited by 124 · see citing works
Revisiting Distillation and Incremental Classifier Learning
Javed, K., & Shafait, F. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-20876-9_1 · Cited by 53 · see citing works
Attributing Fake Images to GANs: Learning and Analyzing GAN FingerprintsPreprint
Yu, N., Davis, L. S., & Fritz, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.08180 · Cited by 38 · see citing works
Attributing Fake Images to GANs: Analyzing Fingerprints in Generated Images.Preprint
Yu, N., Davis, L. S., & Fritz, M. (2018) · arXiv (Cornell University) · Cited by 33 · see citing works
Source Generator Attribution via InversionPreprint
Albright, M., & McCloskey, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.02259 · Cited by 32 · see citing works
On the generalization of GAN image forensicsPreprint
Xuan, X., Peng, B., Wang, W., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11153 · Cited by 10 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [1]1. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative Adversarial Nets," in NIPS, 2014, pp. 2672-2680.
- [6]6. T. Karras, T. Aila, S. Laine, and J. Lehtinen, "Progressive Growing of GANs for Improved Quality, Stability, and Variation," in ICLR, 2018.
- [23]23. D. P. Kingma and P. Dhariwal, "Glow: generative flow with invertible 1×1 convolutions," in NIPS, 2018, pp. 10 236-10 245.
Cited by
Sorted by citations
Deepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237 · see citing works
Face X-Ray for More General Face Forgery Detection
Li, L., Bao, J., Zhang, T., Yang, H., Chen, D., Wen, F., & Guo, B. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00505 · Cited by 1,211 · see citing works
Media Forensics and DeepFakes: An Overview
Verdoliva, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3002101 · Cited by 979 · see citing works
Self-citationMasood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543 · see citing works
Deep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464
Deep learning for deepfakes creation and detection: A survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103525 · Cited by 464 · see citing works
“, in [104, 116, 160, 169, 170], but more work needs to be done in this direction.”
BackgroundGlobal Texture Enhancement for Fake Face Detection in the Wild
Liu, Z., Qi, X., & Torr, P. H. S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00808 · Cited by 401 · see citing works
Are GAN Generated Images Easy to Detect? A Critical Analysis of the State-Of-The-Art
Gragnaniello, D., Cozzolino, D., Marra, F., Poggi, G., & Verdoliva, L. (2021) · 2021 IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428429 · Cited by 212 · see citing works
Self-citationCountering Malicious DeepFakes: Survey, Battleground, and Horizon
Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., & Liu, Y. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01606-8 · Cited by 191 · see citing works
“Other studies (Marra et al., 2019a) also leverage the GAN fingerprints for discriminating GAN-synthesized fake faces.”
BackgroundResultForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis
He, Y., Gan, B., Chen, S., Zhou, Y., Yin, G., Song, L., Sheng, L., Shao, J., & Liu, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00434 · Cited by 161 · see citing works
A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats
Mubarak, R., Alsboui, T., Alshaikh, O., Inuwa-Dutse, I., Khan, S., & Parkinson, S. (2023) · IEEE Access · doi:10.1109/access.2023.3344653 · Cited by 132 · see citing works
Optical Flow based CNN for detection of unlearnt deepfake manipulations
Caldelli, R., Galteri, L., Amerini, I., & Del Bimbo, A. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.005 · Cited by 115 · see citing works
A detailed analysis of image and video forgery detection techniques
Tyagi, S., & Yadav, D. (2023) · The Visual Computer · doi:10.1007/s00371-021-02347-4 · Cited by 109 · see citing works
Deepfakes generation and detection: a short survey
Akhtar, Z. (2023) · Journal of Imaging · doi:10.3390/jimaging9010018 · Cited by 106 · see citing works
Deepfake Media Forensics: Status and Future Challenges
Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bruni, V., Caldelli, R., De Natale, F., De Nicola, R., Guarnera, L., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11030073 · Cited by 113 · see citing works
Self-citationFakeLocator: Robust Localization of GAN-Based Face Manipulations
Huang, Y., Juefei-Xu, F., Guo, Q., Liu, Y., & Pu, G. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3141262 · Cited by 102 · see citing works
CNN Detection of GAN-Generated Face Images based on Cross-Band Co-occurrences Analysis
Barni, M., Kallas, K., Nowroozi, E., & Tondi, B. (2020) · 2020 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs49906.2020.9360905 · Cited by 103 · see citing works
GAN-Generated Faces Detection: A Survey and New Perspectives
Wang, X., Guo, H., Hu, S., Chang, M.-C., & Lyu, S. (2023) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/faia230558 · Cited by 98 · see citing works
Exploiting Prediction Error Inconsistencies through LSTM-based Classifiers to Detect Deepfake Videos
Amerini, I., & Caldelli, R. (2020) · Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/3369412.3395070 · Cited by 64 · see citing works
Identifying and Mitigating the Security Risks of Generative AI
Barrett, C., Boyd, B., Bursztein, E., Carlini, N., Chen, B., Choi, J., Chowdhury, A. R., Christodorescu, M., Datta, A., Feizi, S., et al. (2023) · Foundations and Trends® in Privacy and Security · doi:10.1561/3300000041 · Cited by 57 · see citing works
Generalization of Forgery Detection With Meta Deepfake Detection Model
Tran, V.-N., Kwon, S.-G., Lee, S.-H., Le, H.-S., & Kwon, K.-R. (2023) · IEEE Access · doi:10.1109/access.2022.3232290 · Cited by 47 · see citing works
“The work in [39] presented a multi-task incremental learning-based methodology for the detection and classification of manipulated images, the model can adapt to new”
MethodLEDNet: a multimodal foundation model for robust deepfake detection
Tao, R., Tang, S., Qin, H., Wang, W., Wei, Y., & Zhao, Y. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4400-8 · Cited by 9 · see citing works
- Authors
- Marra, F., Saltori, C., Boato, G., & Verdoliva, L.
- Affiliations
- University of Naples Federico II (IT)
- University of Trento (IT)
- Venue
- 2019 IEEE International Workshop on Information Forensics and Security (WIFS)
- Publisher
- IEEE
- Published
- 2019-12
- Pages
- 1-6
- ISSN
- 2157-4774 (ISSN-L)
- OpenAlex
- W2978778164
- Cited by
- 154
- References
- 25
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 30 · 2023: 25 · 2022: 30 · 2021: 22 · 2020: 13
- FWCI
- 5.24
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Workshop on Information Forensics and Security (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- arXiv (Cornell University)
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- GAN-generated image detection, generative adversarial networks, incremental learning, continual learning, image classification, face synthesis, adversarial robustness
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