CoChat
Sign Up
Cited by 115Digital Media Forensic DetectionBiometric Identification and SecurityFace recognition and analysis

Beyond the Prior Forgery Knowledge: Mining Critical Clues for General Face Forgery Detection

Open access
Luo, Anwei, Kong, Chenqi, Huang, Jiwu, Hu, Yongjian, Kang, Xiangui, Kot, Alex C.·2024·IEEE Transactions on Information Forensics and Security
DOI 10.1109/tifs.2023.3332218
Open this paper in CoChat — free accountOpen PDF

Abstract

Face forgery detection is essential in combating malicious digital face attacks. Previous methods mainly rely on prior expert knowledge to capture specific forgery clues, such as noise patterns, blending boundaries, and frequency artifacts. However, these methods tend to get trapped in local optima, resulting in limited robustness and generalization capability. To address these issues, we propose a novel Critical Forgery Mining (CFM) framework, which can be flexibly assembled with various backbones to boost their generalization and robustness performance. Specifically, we first build a fine-grained triplet and suppress specific forgery traces through prior knowledge-agnostic data augmentation. Subsequently, we propose a fine-grained relation learning prototype to mine critical information in forgeries through instance and local similarity-aware losses. Moreover, we design a novel progressive learning controller to guide the model to focus on principal feature components, enabling it to learn critical forgery features in a coarse-to-fine manner. The proposed method achieves state-of-the-art forgery detection performance under various challenging evaluation settings. The source code is available at:https://github.com/LoveSiameseCat/CFM.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tifs.2023.3332218

Cites

18 of 18 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Depending on specific modalities, augmentation operations are various, including (i) image modality: Gaussian blur, JPEG compression, random crop; (ii) video modality: temporal dropout, temporal repeat, clip-level blending; (iii) audio modality: band-pass filters, timeand pitch-shifting, RawBoost.

    “Depending on specific modalities, augmentation operations are various, including (i) image modality: Gaussian blur, JPEG compression, random crop [159, 160]; (ii) video modality: temporal dropout, temporal repeat, clip-level blending [161]; (iii) audio modality: band-pass filters, timeand pitch-shifting, RawBoost [151, 154].”

    From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [160]

    Split into 3 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

    Experimental — accuracy not yet measured

  • Data augmentation significantly improves detection robustness by exposing models to various distortion techniques they might encounter in real-world scenarios.

    “Multiple studies have demonstrated that data augmentation significantly improves detection robustness by exposing models to various distortion techniques they might encounter in real-world scenarios [160, 161, 164].”

    From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [160]

  • Existing face forgery detection methods lack generalization capability and tend to suffer dramatic performance drops when deployed to unforeseen domains.

    “Despite the demonstrated success in intra-domain face forgery detection, existing detection methods lack generalization capability and tend to suffer from dramatic performance drops when deployed to unforeseen domains.”

    From Enhancing General Face Forgery Detection via Vision Transformer with Low-Rank Adaptation (2023) · cites this paper as [31]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 63

  1. [3][3] J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner, "Face2face: Real-time face capture and reenactment of rgb videos," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2387–2395.
  2. [4][4] J. Thies, M. Zollhöfer, and M. Nießner, "Deferred neural rendering: Image synthesis using neural textures," Acm Transactions on Graphics (TOG), vol. 38, no. 4, pp. 1–12, 2019.
  3. [7][7] C. Kong, S. Wang, and H. Li, "Digital and physical face attacks: Reviewing and one step further," arXiv preprint arXiv:2209.14692, 2022. · arXiv
  4. [8][8] Y. Li, M.-C. Chang, and S. Lyu, "In ictu oculi: Exposing ai created fake videos by detecting eye blinking," in 2018 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2018, pp. 1–7.
  5. [9][9] X. Yang, Y. Li, and S. Lyu, "Exposing deep fakes using inconsistent head poses," in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 8261–8265.
  6. [10][10] Y. Li and S. Lyu, "Exposing deepfake videos by detecting face warping artifacts," arXiv preprint arXiv:1811.00656, 2018. · arXiv
  7. [11][11] H. H. Nguyen, J. Yamagishi, and I. Echizen, "Capsule-forensics: Using capsule networks to detect forged images and videos," in ICASSP 2019- 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 2307–2311.
  8. [12][12] R. Cai, H. Li, S. Wang, C. Chen, and A. C. Kot, "Drl-fas: A novel framework based on deep reinforcement learning for face anti-spoofing," IEEE Transactions on Information Forensics and Security, vol. 16, pp. 937–951, 2020.
  9. [13][13] R. Cai, Z. Li, R. Wan, H. Li, Y. Hu, and A. C. Kot, "Learning meta pattern for face anti-spoofing," IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1201–1213, 2022. https://github.com/deepfakes/faceswap https://github.com/deepfakes/faceswap https://github.com/deepfakes/faceswap https://www.malavida.com/en/soft https://play.google.com/store/apps/details?id=video.reface.app&gl=US&pli=1 https://play.google.com/store/apps/details?id=video.reface.app&gl=US&pli=1
  10. [14][14] D. Afchar, V. Nozick, J. Yamagishi, and I. Echizen, "Mesonet: a compact facial video forgery detection network," in 2018 IEEE International Workshop on Information Forensics and Security (WIFS). IEEE, 2018, pp. 1–7.
  11. [15][15] S. Tariq, S. Lee, and S. S. Woo, "A convolutional lstm based residual network for deepfake video detection," arXiv preprint arXiv:2009.07480, 2020. · arXiv
  12. [16][16] Q. Gu, S. Chen, T. Yao, Y. Chen, S. Ding, and R. Yi, "Exploiting fine- grained face forgery clues via progressive enhancement learning," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 1, 2022, pp. 735–743.
  13. [17][17] H. Zhao, W. Zhou, D. Chen, T. Wei, W. Zhang, and N. Yu, "Multi- attentional deepfake detection," in Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, 2021, pp. 2185– 2194.
  14. [19][19] Y. Yu, X. Zhao, R. Ni, S. Yang, Y. Zhao, and A. C. Kot, "Augmented multi-scale spatiotemporal inconsistency magnifier for generalized deep- fake detection," IEEE Transactions on Multimedia, pp. 1–13, 2023.
  15. [20][20] Y. Luo, Y. Zhang, J. Yan, and W. Liu, "Generalizing face forgery detec- tion with high-frequency features," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 16 317–16 326.
  16. [21][21] I. Masi, A. Killekar, R. M. Mascarenhas, S. P. Gurudatt, and W. Ab- dAlmageed, "Two-branch recurrent network for isolating deepfakes in videos," in European Conference on Computer Vision. Springer, 2020, pp. 667–684.
  17. [22][22] C. Kong, B. Chen, H. Li, S. Wang, A. Rocha, and S. Kwong, "Detect and locate: Exposing face manipulation by semantic-and noise-level telltales," IEEE Transactions on Information Forensics and Security, vol. 17, pp. 1741–1756, 2022.
  18. [23][23] L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, and B. Guo, "Face x-ray for more general face forgery detection," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 5001–5010.
  19. [25][25] K. Xu, M. Qin, F. Sun, Y. Wang, Y.-K. Chen, and F. Ren, "Learning in the frequency domain," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 1740–1749.
  20. [26][26] J. Li, H. Xie, J. Li, Z. Wang, and Y. Zhang, "Frequency-aware discrim- inative feature learning supervised by single-center loss for face forgery detection," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 6458–6467.
  21. [28][28] Z. Mi, X. Jiang, T. Sun, and K. Xu, "Gan-generated image detection with self-attention mechanism against gan generator defect," IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 5, pp. 969–981, 2020.
  22. [29][29] R. Durall, M. Keuper, and J. Keuper, "Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 7890–7899.
  23. [30][30] T. Dzanic, K. Shah, and F. Witherden, "Fourier spectrum discrepancies in deep network generated images," Proceedings of Advances in Neural Information Processing Systems, vol. 33, pp. 3022–3032, 2020.
  24. [32][32] A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, and M. Nießner, "Faceforensics++: Learning to detect manipulated facial images," in Proceedings of the IEEE/CVF International Conference on Ccomputer Vision, 2019, pp. 1–11.
  25. [35][35] B. Zi, M. Chang, J. Chen, X. Ma, and Y.-G. Jiang, "Wilddeepfake: A challenging real-world dataset for deepfake detection," in Proceedings of the 28th ACM International Conference on Multimedia, 2020, pp. 2382–2390.
  26. [36][36] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-cam: Visual explanations from deep networks via gradient-based localization," in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 618–626.
  27. [37][37] F. Matern, C. Riess, and M. Stamminger, "Exploiting visual artifacts to expose deepfakes and face manipulations," in 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). IEEE, 2019, pp. 83–92.
  28. [39][39] F. Chollet, "Xception: Deep learning with depthwise separable convolu- tions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1251–1258.
  29. [40][40] K. Sun, H. Liu, T. Yao, X. Sun, S. Chen, S. Ding, and R. Ji, "An infor- mation theoretic approach for attention-driven face forgery detection," in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XIV. Springer, 2022, pp. 111–127.
  30. [41][41] Z. Yu, R. Cai, Z. Li, W. Yang, J. Shi, and A. C. Kot, "Benchmarking joint face spoofing and forgery detection with visual and physiological cues," arXiv preprint arXiv:2208.05401, 2022. · arXiv
  31. [42][42] Y. Yu, R. Ni, W. Li, and Y. Zhao, "Detection of ai-manipulated fake faces via mining generalized features," ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 18, no. 4, pp. 1–23, 2022.
  32. [43][43] X. Liu, Y. Yu, X. Li, Y. Zhao, and G. Guo, "Tcsd: Triple complementary streams detector for comprehensive deepfake detection," ACM Trans- actions on Multimedia Computing, Communications, and Applications (TOMM), 2022.
  33. [45][45] Z. Yu, C. Zhao, Z. Wang, Y. Qin, Z. Su, X. Li, F. Zhou, and G. Zhao, "Searching central difference convolutional networks for face anti- spoofing," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 5295–5305.
  34. [46][46] Y. Qian, G. Yin, L. Sheng, Z. Chen, and J. Shao, "Thinking in frequency: Face forgery detection by mining frequency-aware clues," in European Conference on Computer Vision. Springer, 2020, pp. 86–103.
  35. [47][47] X. Dong, J. Bao, D. Chen, T. Zhang, W. Zhang, N. Yu, D. Chen, F. Wen, and B. Guo, "Protecting celebrities from deepfake with identity consistency transformer," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 9468–9478.
  36. [48][48] P. Yu, J. Fei, Z. Xia, Z. Zhou, and J. Weng, "Improving generalization by commonality learning in face forgery detection," IEEE Transactions on Information Forensics and Security, vol. 17, pp. 547–558, 2022.
  37. [50][50] J. Wang, Y. Sun, and J. Tang, "Lisiam: Localization invariance siamese network for deepfake detection," IEEE Transactions on Information Forensics and Security, vol. 17, pp. 2425–2436, 2022.
  38. [52][52] S. Chen, T. Yao, Y. Chen, S. Ding, J. Li, and R. Ji, "Local relation learn- ing for face forgery detection," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 2, 2021, pp. 1081–1088.
  39. [53][53] C. Wang and W. Deng, "Representative forgery mining for fake face detection," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 14 923–14 932.
  40. [54][54] Y. Tian, D. Krishnan, and P. Isola, "Contrastive multiview coding," in European Conference on Computer Vision. Springer, 2020, pp. 776–
  41. [794]794. [55] J. Zhang and K. Ma, "Rethinking the augmentation module in contrastive learning: Learning hierarchical augmentation invariance with expanded views," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16 650–16 659.
  42. [56][56] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, "A simple framework for contrastive learning of visual representations," in International Conference on Machine Learning. PMLR, 2020, pp. 1597–1607.
  43. [57][57] K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, "Momentum contrast for unsupervised visual representation learning," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729–9738. https://github.com/ MarekKowalski/FaceSwap/ https://https://github.com/deepfakes/
  44. [58][58] X. Chen and K. He, "Exploring simple siamese representation learning," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 15 750–15 758.
  45. [59][59] J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar et al., "Bootstrap your own latent-a new approach to self-supervised learning," Proceedings of Advances in Neural Information Processing Systems, vol. 33, pp. 21 271–21 284, 2020.
  46. [61][61] P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, "Supervised contrastive learn- ing," Proceedings of Advances in Neural Information Processing Sys- tems, vol. 33, pp. 18 661–18 673, 2020.
  47. [63][63] Y. Dai, J. Fei, H. Wang, and Z. Xia, "Attentional local contrastive learning for face forgery detection," in Artificial Neural Networks and Machine Learning–ICANN 2022: 31st International Conference on Arti- ficial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings, Part I. Springer, 2022, pp. 709–721.
  48. [65][65] H. Zhao, W. Zhou, D. Chen, W. Zhang, and N. Yu, "Self-supervised transformer for deepfake detection," arXiv preprint arXiv:2203.01265, 2022. · arXiv
  49. [66][66] C. Kong, B. Chen, W. Yang, H. Li, P. Chen, and S. Wang, "Appearance matters, so does audio: Revealing the hidden face via cross-modality transfer," IEEE Transactions on Circuits and Systems for Video Tech- nology, vol. 32, no. 1, pp. 423–436, 2021.
  50. [67][67] J. Fei, Y. Dai, P. Yu, T. Shen, Z. Xia, and J. Weng, "Learning second or- der local anomaly for general face forgery detection," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20 270–20 280.
  51. [68][68] Y. Nirkin, L. Wolf, Y. Keller, and T. Hassner, "Deepfake detection based on discrepancies between faces and their context," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
  52. [70][70] S.-Y. Wang, O. Wang, R. Zhang, A. Owens, and A. A. Efros, "Cnn- generated images are surprisingly easy to spot... for now," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion, 2020, pp. 8695–8704.
  53. [77][77] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, "Joint face detection and alignment using multitask cascaded convolutional networks," IEEE signal processing letters, vol. 23, no. 10, pp. 1499–1503, 2016. els: Weight-averaged consistency targets improve semi-supervised deep learning results," Advances in neural information processing systems, vol. 30, 2017.
  54. [72][72] B. Shi, D. Zhang, Q. Dai, Z. Zhu, Y. Mu, and J. Wang, "Informative dropout for robust representation learning: A shape-bias perspective," in International Conference on Machine Learning. PMLR, 2020, pp. 8828–8839.
  55. [73][73] Y. Suh, B. Han, W. Kim, and K. M. Lee, "Stochastic class-based hard example mining for deep metric learning," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 7251–7259.
  56. [74][74] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, "Focal loss for dense object detection," in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 2980–2988.
  57. [75][75] K. Sun, H. Liu, Q. Ye, Y. Gao, J. Liu, L. Shao, and R. Ji, "Domain general face forgery detection by learning to weight," in Proceedings of the AAAI conference on Artificial Intelligence, vol. 35, no. 3, 2021, pp. 2638–2646.
  58. [76][76] Z. Yang, J. Liang, Y. Xu, X.-Y. Zhang, and R. He, "Masked relation learning for deepfake detection," IEEE Transactions on Information Forensics and Security, 2023.
  59. [78][78] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., "Pytorch: An imperative style, high-performance deep learning library," Advances in neural information processing systems, vol. 32, 2019.
  60. [79][79] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009, pp. 248–255.
  61. [81][81] Y. Zheng, J. Bao, D. Chen, M. Zeng, and F. Wen, "Exploring temporal coherence for more general video face forgery detection," in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 15 044–15 054.
  62. [86][86] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "Mobilenetv2: Inverted residuals and linear bottlenecks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510–4520.
  63. [87][87] L. Van der Maaten and G. Hinton, "Visualizing data using t-sne." Journal of machine learning research, vol. 9, no. 11, 2008. https://www.mdpi.com/2078-2489/11/2/125 https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html I Introduction II Related Work II-A Prior Knowledge-based Face Forgery Detection II-B Face Forgery Detection via Representation Learning III Proposed Method III-A Data Preparation III-A1 Fine-grained Triplet Construction III-A2 Prior Knowledge-agnostic Data Augmentation III-B Fine-grained Triplet Relation Learning III-B1 Progressive Learning Controller III-C Objective Functions III-C1 Instance Similarity-aware Loss III-C2 Local Similarity-aware Loss III-C3 Total Loss IV Experiments IV-A Experimental Setups IV-A1 Implementation Details IV-A2 Evaluation Metrics IV-A3 Dataset IV-B Intra-dataset Evaluation IV-C Generalization Evaluation IV-C1 Cross-dataset Evaluation IV-C2 Cross-manipulation Evaluation IV-D Robustness Evaluation IV-E Flexibility Evaluation IV-F Ablation Experiments IV-F1 Impacts of dins, spos and sneg IV-F2 Effectiveness of Different Proposed Components IV-F3 Impacts of Different Masking Strategies for PLC IV-F4 Impacts of the Prior Knowledge-agnostic Augmentation IV-F5 Effectiveness of Pairwise Constraint for Anchor-Negative data Pair IV-G Visualization IV-G1 Visualization for Detector's Attention IV-G2 T-SNE Feature Embedding Visualization V Conclusion and Future work References

Cited by

Sorted by citations

Authors
Luo, A., Kong, C., Huang, J., Hu, Y., Kang, X., & Kot, A. C.
Affiliations
  • Sun Yat-sen University (CN)
  • City University of Hong Kong (HK)
  • Shenzhen University (CN)
  • South China University of Technology (CN)
  • Nanyang Technological University (SG)
ORCID
Venue
IEEE Transactions on Information Forensics and Security
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2024
Volume
19
Pages
1168-1182
ISSN
  • 1556-6013 (ISSN-L)
  • 1556-6021
DOI
10.1109/tifs.2023.3332218
OpenAlex
W4388623432
Cited by
115
References
18
Citations by year
2026: 54 · 2025: 40 · 2024: 18 · 2023: 3
FWCI
10.44
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Information Forensics and Security (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • DR-NTU (Nanyang Technological University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • NSFC (62072484, U19B2022)
  • Science and Technology Foundation of Guangzhou Huangpu Development District (2022GH15)
  • Rapid-Rich Object Search (ROSE) Laboratory, Nanyang Technological University, Singapore
  • China–Singapore International Joint Research Institute (206-A018001)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
Keywords
face forgery detection, triplet learning, data augmentation, relation learning, progressive learning, generalization, robustness

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