Analyzing and Improving the Image Quality of StyleGAN
Open accessAbstract
The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.
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StyleGAN2 helps detect image deepfakes by determining whether a picture is generated by a network.
“StyleGAN2 is also helping detect image deepfakes as it can see if the picture is generated by a network or not (Karras et al., 2020).”
From Audio deepfakes: A survey (2023) · cites this paper as Karras et al., 2020
Section 5.1 reports that StyleGAN2-generated images can be projected into its latent space and almost unambiguously attributed to the generating network. That supports identifying images made by StyleGAN2, but not the broader claim that StyleGAN2 determines whether any picture was generated by a network; the section discusses general fake-image detection as a separate task performed by classifiers. (LLM, conf=0.86)
Experimental — accuracy not yet measuredStyleGAN2 has exposed several typical artifacts of StyleGAN and has proposed changes in both model architecture and training methods to address them.
“StyleGAN2 (Karras et al. 2020) has exposed several typical artifacts of StyleGAN and has proposed changes in both model architecture and training methods to address them.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Karras et al. 2020
In ForgeryNet, fake images are manipulated using 15 different manipulations while videos are manipulated using only 8 of them.
“The fake images are manipulated using 15 different manipulations while the videos are manipulated using only 8 of them [27,28,29,30,31,32,33,34,35,36].”
From On the Generalization of Deep Learning Models in Video Deepfake Detection (2023) · cites this paper as [30]
Section 5.1, “Attribution of generated images,” discusses detecting and attributing GAN-generated images, but does not describe ForgeryNet. Searches for ForgeryNet, the 15 image manipulations, and the 8 video manipulations found no passage addressing those counts in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredLayerNorm prevents large magnitudes from appearing in specific regions of the latent space or reconstructed outputs.
“LayerNorm) (Lei Ba et al., 2016) instead of Group Normalization (GroupNorm) (Wu and He, 2018), which prevents large magnitudes from appearing in specific regions of the latent space or reconstructed outputs (Karras et al., 2020; Sadat et al., 2024).”
From Cosmos World Foundation Model Platform for Physical AI (2025) · cites this paper as Karras et al., 2020
Section 2 describes localized spikes in StyleGAN feature maps and reports that redesigned normalization removes the resulting artifacts. It does not evaluate LayerNorm versus GroupNorm or say that LayerNorm prevents large magnitudes in latent-space regions or reconstructed outputs (searched: LayerNorm, layer normalization, GroupNorm, reconstructed outputs). (LLM, conf=0.75)
Experimental — accuracy not yet measured
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“StyleGAN2 (Karras et al., 2020) has exposed several typical artifacts of StyleGAN and has proposed changes in both model architecture and training methods”
BackgroundArtificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
Yu, N., Skripniuk, V., Abdelnabi, S., & Fritz, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01418 · Cited by 195 · see citing works
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms
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Fighting Deepfake by Exposing the Convolutional Traces on Images
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Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash
Struppek, L., Hintersdorf, D., Neider, D., & Kersting, K. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533073 · Cited by 153 · see citing works
Dolezal, J. M., Srisuwananukorn, A., Karpeyev, D., Ramesh, S., Kochanny, S., Cody, B., Mansfield, A. S., Rakshit, S., Bansal, R., Bois, M. C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34025-x · Cited by 147 · see citing works
Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
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FrePGAN: Robust Deepfake Detection Using Frequency-Level Perturbations
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A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features
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RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
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Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
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Fighting Deepfakes by Detecting GAN DCT Anomalies
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A detailed analysis of image and video forgery detection techniques
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Deepfakes generation and detection: a short survey
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The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
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Deepfake Media Forensics: Status and Future Challenges
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FakeLocator: 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
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Testing human ability to detect ‘deepfake’ images of human faces
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GAN-Generated Faces Detection: A Survey and New Perspectives
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Opal: Multimodal Image Generation for News Illustration
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Object Detection with Transformers: A Review
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Phenaki: Variable Length Video Generation From Open Domain Textual Description
Villegas, R., Babaeizadeh, M., Kindermans, P.-J., Moraldo, H., Zhang, H., Saffar, M. T., Castro, S., Kunze, J., & Erhan, D. (2022) · arXiv · doi:10.48550/arxiv.2210.02399 · Cited by 92 · see citing works
“The discriminator architecture is the StyleGAN [21] discriminator with blur resample, and channel multiplier of 1 .”
BackgroundMethodA Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection
Chandrasegaran, K., Tran, N.-T., & Cheung, N.-M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00712 · Cited by 90 · see citing works
Warning: Humans cannot reliably detect speech deepfakes
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“Modality Year Author Deepfake stimuli Image 2021 Nightingale & Farid [23] Faces generated using StyleGAN2 [31] 2022 Bray et al.”
MethodTransformers for Vision: A Survey on Innovative Methods for Computer Vision
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X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
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Self-consuming generative models go MAD
Casco-Rodriguez, J., Alemohammad, S., Luzi, L., Imtiaz, A., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R. (2023) · LatinX in AI at Neural Information Processing Systems Conference 2023 · doi:10.52591/lxai202312101 · Cited by 83 · see citing works
The face deepfake detection challenge
Guarnera, L., Giudice, O., Guarnera, F., Ortis, A., Puglisi, G., Paratore, A., Bui, L. M. Q., Fontani, M., Coccomini, D. A., Caldelli, R., et al. (2022) · Journal of Imaging · doi:10.3390/jimaging8100263 · Cited by 79 · see citing works
Seeing is Believing: Exploring Perceptual Differences in DeepFake Videos
Tahir, R., Batool, B., Jamshed, H., Jameel, M., Anwar, M., Ahmed, F., Zaffar, M. A., & Zaffar, M. F. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445699 · Cited by 79 · see citing works
Exploring Generative Models with Middle School Students
Ali, S., DiPaola, D., Lee, I., Hong, J., & Breazeal, C. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445226 · Cited by 82 · see citing works
Human detection of political speech deepfakes across transcripts, audio, and video
Groh, M., Sankaranarayanan, A., Singh, N., Kim, D. Y., Lippman, A., & Picard, R. (2024) · Nature Communications · doi:10.1038/s41467-024-51998-z · Cited by 89 · see citing works
Mastering Deepfake Detection: A Cutting-Edge Approach to Distinguish GAN and Diffusion-Model Images
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Anatomy of an AI-powered malicious social botnet
Yang, K., & Menczer, F. (2024) · Journal of Quantitative Description: Digital Media · doi:10.51685/jqd.2024.icwsm.7 · Cited by 71 · see citing works
Data-free Knowledge Distillation for Object Detection
Chawla, A., Yin, H., Molchanov, P., & Alvarez, J. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00333 · Cited by 70 · see citing works
Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis
He, Y., Yu, N., Keuper, M., & Fritz, M. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/349 · Cited by 70 · see citing works
DeepFake Detection Based on High-Frequency Enhancement Network for Highly Compressed Content
Gao, J., Xia, Z., Marcialis, G. L., Dang, C., Dai, J., & Feng, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123732 · Cited by 67 · see citing works
Depth map guided triplet network for deepfake face detection
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Assessment framework for deepfake detection in real-world situations
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“For example, the recent advancement of generative adversarial networks (GANs) [1, 2, 3] has made it possible to generate realistic forged contents that are difficult for humans to distinguish from their authentic counterparts.”
BackgroundSpoC: Spoofing Camera Fingerprints
Cozzolino, D., Thies, J., Rossler, A., Niesner, M., & Verdoliva, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00110 · Cited by 54 · see citing works
Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification
Montero, A., Bonet-Carne, E., & Burgos-Artizzu, X. P. (2021) · Sensors · doi:10.3390/s21237975 · Cited by 48 · see citing works
Deepfake Video Detection with Spatiotemporal Dropout Transformer
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Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning
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Initial Images: Using Image Prompts to Improve Subject Representation in Multimodal AI Generated Art
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Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
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GANCollage: A GAN-Driven Digital Mood Board to Facilitate Ideation in Creativity Support
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AI or Human: The Socio-ethical Implications of AI-Generated Media Content
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Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects
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Stochastic Image Denoising by Sampling from the Posterior Distribution
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Threats and Defenses in the Federated Learning Life Cycle: A Comprehensive Survey and Challenges
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Diffusion-Based Visual Art Creation: A Survey and New Perspectives
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- Authors
- Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T.
- Affiliations
- Nvidia (United Kingdom) (GB)
- Aalto University (FI)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 8107-8116
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2995327724
- Semantic Scholar
- 14fdc18d9c164e5b0d6d946b3238c04e81921358
- Cited by
- 6,332
- References
- 56
- Citations by year
- 2026: 463 · 2025: 1070 · 2024: 1389 · 2023: 1362 · 2022: 1009 · 2021: 792 · 2020: 243 · 2019: 4
- FWCI
- 262.37
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- StyleGAN, image quality, unconditional image generation, model capacity
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 ›
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