Global Texture Enhancement for Fake Face Detection in the Wild
Open accessAbstract
Generative Adversarial Networks (GANs) can generate realistic fake face images that can easily fool human beings. On the contrary, a common Convolutional Neural Network(CNN) discriminator can achieve more than99.9%accuracyin discerning fake/real images. In this paper, we conduct an empirical study on fake/real faces, and have two important observations: firstly, the texture of fake faces is substantially different from real ones; secondly, global texture statistics are more robust to image editing and transferable to fake faces from different GANs and datasets. Motivated by the above observations, we propose a new architecture coined as Gram-Net, which leverages global image texture representations for robust fake image detection. Experimental results on several datasets demonstrate that our Gram-Netoutperforms existing approaches. Especially, our Gram-Netis more robust to image editings, e.g. down-sampling, JPEGcompression, blur, and noise. More importantly, our Gram-Net generalizes significantly better in detecting fake faces from GAN models not seen in the training phase and can perform decently in detecting fake natural images.
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?
Common visual artifacts include lighting and color mismatches, unusual facial textures, irregular frequency patterns, and inconsistent edge blending.
“Common visual artifacts include lighting and color mismatches [51], unusual facial textures [52], irregular frequency patterns [56], and inconsistent edge blending [59].”
From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [52]
The frequency spectrum of GAN-generated images often contains distinctive patterns - 'checkerboards', particularly in the middle and high-frequency bands.
“Several studies have shown that the frequency spectrum of GAN-generated images often contains distinctive patterns - 'checkerboards', particularly in the middle and high-frequency bands [85-89].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [88]
Section 3.3 analyzes differences in texture contrast between real and GAN-generated images using gray-level co-occurrence matrices, not frequency spectra. Searches for checkerboards, Fourier spectra, and middle- or high-frequency bands found no passage addressing the claimed spectral pattern. (LLM, conf=0.75)
Experimental — accuracy not yet measured
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · 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
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
(2017) · Cited by 11,625 · see citing works
Learning Deep Features for Discriminative Localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.319 · Cited by 11,428 · see citing works
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747 · see citing works
Self-citationImproved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800 · see citing works
(2017) · International Conference on Machine Learning · Cited by 6,382 · see citing works
Image Style Transfer Using Convolutional Neural Networks
Gatys, L. A., Ecker, A. S., & Bethge, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.265 · Cited by 6,025 · see citing works
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017) · arXiv · doi:10.48550/arxiv.1710.10196 · Cited by 4,727 · 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
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
Dlib-ml: A Machine Learning Toolkit
King, D. E. (2009) · Journal of machine learning research · doi:10.5555/1577069.1755843 · Cited by 2,945 · see citing works
Unsupervised Image-to-Image Translation NetworksPreprint
Liu, M.-Y., Breuel, T. M., & Kautz, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00848 · Cited by 2,121 · see citing works
Understanding deep image representations by inverting them
Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827 · see citing works
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Luo, W., Li, Y., Urtasun, R., & Zemel, R. (2017) · arXiv · doi:10.48550/arxiv.1701.04128 · Cited by 1,575 · see citing works
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., & Brendel, W. (2018) · arXiv · doi:10.48550/arxiv.1811.12231 · Cited by 1,490 · 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
Texture Synthesis Using Convolutional Neural NetworksPreprint
Gatys, L. A., Ecker, A. S., & Bethge, M. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.07376 · Cited by 902 · see citing works
Image smoothing via L 0 gradient minimization
Xu, L., Lu, C., Xu, Y., & Jia, J. (2011) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/2024156.2024208 · Cited by 886 · see citing works
Image smoothing via L 0 gradient minimization
Li, X., Lu, C., Xu, Y., & Jia, J. (2011) · ACM Transactions on Graphics · doi:10.1145/2070781.2024208 · Cited by 786 · see citing works
Exposing DeepFake Videos By Detecting Face Warping Artifacts
Li, Y., & Lyu, S. (2018) · arXiv · doi:10.48550/arxiv.1811.00656 · Cited by 785 · see citing works
Detecting and Simulating Artifacts in GAN Fake Images
Zhang, X., Karaman, S., & Chang, S.-F. (2019) · 2019 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs47025.2019.9035107 · Cited by 567 · 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
Do 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
Detecting 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
Mayer, G. (2017) · De Gruyter eBooks · doi:10.1515/9783110499469-009 · Cited by 235 · see citing works
On 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
Incremental learning for the detection and classification of GAN-generated images
Marra, F., Saltori, C., Boato, G., & Verdoliva, L. (2019) · 2019 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs47025.2019.9035099 · Cited by 154 · 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
How to Train Your DRAGANPreprint
Kodali, N., Abernethy, J., Hays, J., & Kira, Z. (2017) · arXiv.org · doi:10.48550/arxiv.1705.07215 · Cited by 89 · see citing works
FakeSpotter: A Simple Baseline for Spotting AI-Synthesized Fake Faces
Wang, R., Ma, L., Juefei-Xu, F., Xie, X., Wang, J., & Liu, Y. (2019) · Cited by 36 · see citing works
Flagged first, then by citations
4 of the 43 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasser- stein generative adversarial networks. In International Con- ference on Machine Learning, pages 214-223, 2017.
Cited by
Sorted by citations
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
Generalizing Face Forgery Detection with High-frequency Features
Luo, Y., Zhang, Y., Yan, J., & Liu, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01605 · Cited by 609 · see citing works
Local Relation Learning for Face Forgery Detection
Chen, S., Yao, T., Chen, Y., Ding, S., Li, J., & Ji, R. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16193 · Cited by 334 · see citing works
DeepFake Detection for Human Face Images and Videos: A Survey
Malik, A., Kuribayashi, M., Abdullahi, S. M., & Khan, A. N. (2022) · IEEE Access · doi:10.1109/access.2022.3151186 · Cited by 297 · see citing works
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2022) · ACM Computing Surveys · doi:10.1145/3453158 · Cited by 295 · see citing works
Representative Forgery Mining for Fake Face Detection
Wang, C., & Deng, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01468 · Cited by 288 · see citing works
A Survey on Deepfake Video Detection
Yu, P., Xia, Z., Fei, J., & Lu, Y. (2021) · IET Biometrics · doi:10.1049/bme2.12031 · Cited by 253 · see citing works
Deepfake video detection: challenges and opportunities
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10810-6 · Cited by 214 · see citing works
“By incorporating global texture features, Gram-Net, as proposed by Liu et al. (2020), increases the stability and generalisation of CNNs. Researchers must optimise network architectures and model pruning techniques to reduce computational burden while maintaining detection accuracy.”
Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
Tan, C., Zhao, Y., Wei, S., Gu, G., & Wei, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01165 · Cited by 203 · see citing works
Countering 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
“Gram-Net (Liu et al., 2020b) OC-FakeDect (Khalid and Woo, 2020) proposes training on real faces with a one-class variational autoencoder (VAE) to Table 7: Summary of existing DeepFake detection methods.”
BackgroundMTD-Net: Learning to Detect Deepfakes Images by Multi-Scale Texture Difference
Yang, J., Li, A., Xiao, S., Lu, W., & Gao, X. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3102487 · Cited by 184 · see citing works
Artificial 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
Zhou, T., Wang, W., Liang, Z., & Shen, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00572 · Cited by 156 · see citing works
ForgeryNet: 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
Self-citationExposing GAN-Generated Faces Using Inconsistent Corneal Specular Highlights
Hu, S., Li, Y., & Lyu, S. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414582 · Cited by 150 · see citing works
A Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception
Chen, B., Liu, X., Zheng, Y., Zhao, G., & Shi, Y.-Q. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3116679 · Cited by 125 · see citing works
Genimage: A million-scale benchmark for detecting ai-generated image
Zhu, M., Chen, H., Yan, Q., Huang, X., Lin, G., Li, W., Tu, Z., Hu, H., Hu, J., & Wang, Y. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-3398 · Cited by 128 · see citing works
Yang, J., Xiao, S., Li, A., Lu, W. B., Gao, X., & Li, Y. (2021) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3133859 · Cited by 117 · see citing works
Fighting Deepfakes by Detecting GAN DCT Anomalies
Giudice, O., Guarnera, L., & Battiato, S. (2021) · Journal of Imaging · doi:10.3390/jimaging7080128 · Cited by 118 · see citing works
Face Forgery Detection by 3D Decomposition
Zhu, X., Wang, H., Fei, H., Lei, Z., & Li, S. Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00295 · Cited by 106 · see citing works
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection
Feng, C., Chen, Z., & Owens, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01011 · Cited by 101 · 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
Eyes Tell All: Irregular Pupil Shapes Reveal GAN-Generated Faces
Hui, G., Hu, S., Wang, X., Chang, M., & Lyu, S. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746597 · Cited by 100 · see citing works
Le, T.-N., Nguyen, H. H., Yamagishi, J., & Echizen, I. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00996 · Cited by 97 · see citing works
Testing human ability to detect ‘deepfake’ images of human faces
Bray, S. D., Johnson, S. D., & Kleinberg, B. (2023) · Journal of Cybersecurity · doi:10.1093/cybsec/tyad011 · Cited by 96 · see citing works
TALL: Thumbnail Layout for Deepfake Video Detection
Xu, Y., Liang, J., Jia, G., Yang, Z., Zhang, Y., & He, R. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02071 · Cited by 109 · see citing works
Artifacts-Disentangled Adversarial Learning for Deepfake Detection
Li, X., Ni, R., Yang, P., Fu, Z., & Zhao, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3217950 · Cited by 94 · see citing works
Detecting Generated Images by Real Images
Liu, B., Yang, F., Bi, X., Xiao, B., Li, W., & Gao, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19781-9_6 · Cited by 84 · see citing works
Detecting computer-generated disinformation
Stiff, H., & Johansson, F. (2022) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00299-5 · Cited by 79 · see citing works
Guo, Z., Jia, Z., Wang, L., Wang, D., Yang, G., & Kasabov, N. K. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3324739 · Cited by 76 · see citing works
MRE-Net: Multi-Rate Excitation Network for Deepfake Video Detection
Pang, G., Zhang, B., Teng, Z., Qi, Z., & Jianping Fan, B. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3239607 · Cited by 79 · see citing works
LiSiam: Localization Invariance Siamese Network for Deepfake Detection
Wang, J., Sun, Y., & Tang, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3186803 · Cited by 73 · see citing works
Locally GAN-generated face detection based on an improved Xception
Chen, B., Ju, X., Xiao, B., Ding, W., Zheng, Y., & de Albuquerque, V. H. C. (2021) · Information Sciences · doi:10.1016/j.ins.2021.05.006 · Cited by 70 · see citing works
Federated Learning with Privacy-preserving and Model IP-right-protection
Yang, Q., Huang, A., Fan, L., Chan, C. S., Lim, J. H., Ng, K. W., Ong, D. S., & Li, B. (2023) · Machine Intelligence Research · doi:10.1007/s11633-022-1343-2 · Cited by 78 · see citing works
Privacy Intelligence: A Survey on Image Privacy in Online Social Networks
Liu, C., Zhu, T., Zhang, J., & Zhou, W. (2022) · ACM Computing Surveys · doi:10.1145/3547299 · Cited by 84 · 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
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 Video Detection Based on EfficientNet-V2 Network
Deng, L., Suo, H., & Li, D. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3441549 · Cited by 72 · see citing works
Supervised Contrastive Learning for Generalizable and Explainable DeepFakes Detection
Xu, Y., Raja, K., & Pedersen, M. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00044 · Cited by 62 · see citing works
Face Forgery Detection via Multi-Feature Fusion and Local Enhancement
Zhang, D., Chen, J., Liao, X., Li, F., Chen, J., & Yang, G. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3390945 · Cited by 65 · see citing works
Huang, Z., Hu, J., Li, X., He, Y., Zhao, X., Peng, B., Wu, B., Huang, X., & Cheng, G. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.02685 · Cited by 62 · see citing works
Deepfake Network Architecture Attribution
Yang, T., Huang, Z., Cao, J., Li, L., & Li, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i4.20391 · Cited by 62 · see citing works
Making DeepFakes More Spurious: Evading Deep Face Forgery Detection via Trace Removal Attack
Liu, C., Chen, H., Zhu, T., Zhang, J., & Zhou, W. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2023.3241604 · Cited by 62 · see citing works
DF-UDetector: An effective method towards robust deepfake detection via feature restoration
Ke, J., & Wang, L. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.001 · Cited by 62 · 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
Exploring Disentangled Content Information for Face Forgery Detection
Liang, J., Shi, H., & Deng, W. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19781-9_8 · Cited by 58 · see citing works
LaRE 2 : Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection
Luo, Y., Du, J., Yan, K., & Ding, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01609 · Cited by 61 · see citing works
Robust Attentive Deep Neural Network for Detecting GAN-Generated Faces
Hui, G., Hu, S., Wang, X., Chang, M., & Lyu, S. (2022) · IEEE Access · doi:10.1109/access.2022.3157297 · Cited by 53 · see citing works
A Survey on Learning to Reject
Zhang, X.-Y., Xie, G.-S., Li, X.-C., Mei, T., & Liu, C.-L. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238024 · Cited by 66 · see citing works
An Information Theoretic Approach for Attention-Driven Face Forgery Detection
Sun, K., Liu, H., Yao, T., Sun, X., Chen, S., Ding, S., & Ji, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19781-9_7 · Cited by 51 · see citing works
Deepfakes as a threat to a speaker and facial recognition: An overview of tools and attack vectors
Firc, A., Malinka, K., & Hanáček, P. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15090 · Cited by 54 · see citing works
On the Frequency Bias of Generative ModelsPreprint
Schwarz, K., Liao, Y., & Geiger, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02447 · Cited by 50 · see citing works
Deepfakes Classification of Faces Using Convolutional Neural Networks
Sharma, J., Sharma, S., Kumar, V., Hussein, H. S., & Alshazly, H. A. (2022) · Traitement du signal · doi:10.18280/ts.390330 · Cited by 54 · see citing works
Gao, J., Micheletto, M., Orrù, G., Concas, S., Feng, X., Marcialis, G. L., & Roli, F. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108450 · Cited by 52 · see citing works
DeepFake-Adapter: Dual-Level Adapter for DeepFake Detection
Shao, R., Wu, T., Nie, L., & Liu, Z. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-024-02274-6 · Cited by 48 · see citing works
Self-citationA Review of Deepfake Techniques: Architecture, Detection and Datasets
Edwards, P., Nebel, J.-C., Greenhill, D., & Liang, X. (2024) · IEEE Access · doi:10.1109/access.2024.3477257 · Cited by 50 · see citing works
Deep fake detection using cascaded deep sparse auto-encoder for effective feature selection
Saravana Balaji, B., Kannan, R. J., Prabu, P., Venkatachalam, K., & Trojovský, P. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1040 · Cited by 48 · see citing works
FFR_FD: Effective and fast detection of DeepFakes via feature point defects
Wang, G., Jiang, Q., Jin, X., & Cui, X. (2022) · Information Sciences · doi:10.1016/j.ins.2022.03.026 · Cited by 49 · see citing works
Wang, G., Jiang, Q., Jin, X., Li, W., & Cui, X. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109114 · Cited by 49 · see citing works
C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection
Tan, C., Tao, R., Liu, H., Gu, G., Wu, B., Zhao, Y., & Wei, Y. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i7.32772 · Cited by 44 · see citing works
Neural Style Palette: A Multimodal and Interactive Style Transfer From a Single Style Image
Virtusio, J. J., Ople, J. J. M., Tan, D. S., Tanveer, M., Kumar, N., & Hua, K. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3087026 · Cited by 44 · see citing works
Face Forgery Detection by 3D Decomposition and Composition Search
Zhu, X., Fei, H., Zhang, B., Zhang, T., Zhang, X., Li, S. Z., & Lei, Z. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3233586 · Cited by 41 · see citing works
Forgery-Aware Adaptive Learning With Vision Transformer for Generalized Face Forgery Detection
Luo, A., Cai, R., Kong, C., Ju, Y., Kang, X., Huang, J., & Kot, A. C. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3522091 · Cited by 41 · see citing works
An Examination of Fairness of AI Models for Deepfake Detection
Trinh, L., & Liu, Y. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/79 · Cited by 44 · see citing works
A Local Perturbation Generation Method for GAN-Generated Face Anti-Forensics
Zhang, H., Chen, B., Wang, J., & Zhao, G. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3207310 · Cited by 39 · see citing works
DeepFake Videos Detection Based on Texture Features
Xu, B., Liu, J., Liang, J., Lu, W., & Zhang, Y. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.016760 · Cited by 40 · see citing works
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
Abdullah, S. M., Cheruvu, A., Kanchi, S., Chung, T., Gao, P., Jadliwala, M. S., & Viswanath, B. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00194 · Cited by 50 · see citing works
Detection of AI-Manipulated Fake Faces via Mining Generalized Features
Yu, Y., Ni, R., Li, W., & Zhao, Y. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3499026 · Cited by 51 · see citing works
Are you for real? Decoding realistic AI-generated faces from neural activity
Moshel, M. L., Robinson, A. K., Carlson, T. A., & Grootswagers, T. (2022) · Vision Research · doi:10.1016/j.visres.2022.108079 · Cited by 37 · see citing works
Adaptive Texture and Spectrum Clue Mining for Generalizable Face Forgery Detection
Liu, J., Xie, J., Wang, Y., & Zha, Z.-J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3344293 · Cited by 38 · see citing works
WATCHER: Wavelet-Guided Texture-Content Hierarchical Relation Learning for Deepfake Detection
Wang, Y., Chen, C., Zhang, N., & Hu, X. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02116-5 · Cited by 38 · see citing works
Improving Generalization of Deepfake Detectors by Imposing Gradient Regularization
Guan, W., Wang, W., Dong, J., & Peng, B. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3396064 · Cited by 38 · see citing works
Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
Xu, Y., Liang, J., Sheng, L., & Zhang, X. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02054-2 · Cited by 41 · see citing works
A data augmentation framework by mining structured features for fake face image detection
Guo, Z., Yang, G., Wang, D., & Zhang, D. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103587 · Cited by 38 · see citing works
Video surveillance in smart cities: current status, challenges & future directions
Sharma, H., & Kanwal, N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19696-6 · Cited by 36 · see citing works
Yadav, A. K., & Vishwakarma, D. K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120898 · Cited by 34 · see citing works
Adaptive Face Forgery Detection in Cross Domain
Song, L., Fang, Z., Li, X., Dong, X., Jin, Z., Chen, Y., & Lyu, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19830-4_27 · Cited by 35 · see citing works
Digital Face Manipulation Creation and Detection: A Systematic Review
Dang, M. H., & Nguyen, T. N. (2023) · Electronics · doi:10.3390/electronics12163407 · Cited by 34 · see citing works
An overview of GAN-DeepFakes detection: proposal, improvement, and evaluation
Aissa, F. B., Hamdi, M., Zaied, M., & Mejdoub, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16761-4 · Cited by 35 · see citing works
Spatial-frequency feature fusion based deepfake detection through knowledge distillation
Wang, B., Wu, X., Wang, F., Zhang, Y., Wei, F., & Song, Z. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108341 · Cited by 34 · see citing works
MFF-Net: Deepfake Detection Network Based on Multi-Feature Fusion
Zhao, L., Zhang, M., Ding, H., & Cui, X. (2021) · Entropy · doi:10.3390/e23121692 · Cited by 32 · see citing works
FCD-Net: Learning to Detect Multiple Types of Homologous Deepfake Face Images
Han, R., Wang, X., Bai, N., Wang, Q., Liu, Z., & Xue, J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3269152 · Cited by 32 · see citing works
Self-citationDetection, Attribution and Localization of GAN Generated Images
Goebel, M. G., Nataraj, L., Nanjundaswamy, T., Mohammed, T. M., Chandrasekaran, S., & Manjunath, B. S. (2021) · Electronic Imaging · doi:10.2352/issn.2470-1173.2021.4.mwsf-276 · Cited by 35 · see citing works
Global Semantic Consistency Network for Image Manipulation Detection
Shi, Z., Shen, X., Chen, H., & Lyu, Y. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3026954 · Cited by 30 · see citing works
Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective
Li, O., Cai, J., Hao, Y., Jiang, X. H., Hu, Y., & Feng, F. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709392 · Cited by 37 · see citing works
Improving GAN-Generated Image Detection Generalization Using Unsupervised Domain Adaptation
Zhang, M., Wang, H., He, P., Malik, A., & Liu, H. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859763 · Cited by 28 · see citing works
Global–Local Facial Fusion Based GAN Generated Fake Face Detection
Xue, Z., Jiang, X., Liu, Q., & Wei, Z. (2023) · Sensors · doi:10.3390/s23020616 · Cited by 32 · see citing works
Exposing fake images generated by text-to-image diffusion models
Xu, Q., Wang, H., Meng, L., Mi, Z., Yuan, J., & Yan, H. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.10.021 · Cited by 28 · see citing works
Artifact feature purification for cross-domain detection of AI-generated images
Meng, Z., Peng, B., Dong, J., Tan, T., & Cheng, H. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104078 · Cited by 27 · see citing works
Generalizable deepfake detection via Spatial Kernel Selection and Halo Attention Network
Guo, S., Li, Q., Gao, M., Zhu, X., & Rida, I. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105582 · Cited by 30 · see citing works
AW-MSA: Adaptively weighted multi-scale attentional features for DeepFake detection
Yadav, A. K., & Vishwakarma, D. K. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107443 · Cited by 27 · see citing works
Metric Learning for Anti-Compression Facial Forgery Detection
Cao, S., Zou, Q., Mao, X., Ye, D., & Wang, Z. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475347 · Cited by 26 · see citing works
Zhang, Y., Pang, Z., Huang, S., Wang, C., & Zhou, X. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00154-8 · Cited by 32 · see citing works
Fake Face Detection using Local Binary Pattern and Ensemble Modeling
Wang, Y., Zarghami, V., & Cui, S. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506460 · Cited by 25 · see citing works
Patch Diffusion: A General Module for Face Manipulation Detection
Zhang, B., Li, S., Feng, G., Qian, Z., & Zhang, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20233 · Cited by 25 · see citing works
Xiao, S., Lan, G., Yang, J., Li, Y., & Wen, J. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3213832 · Cited by 26 · see citing works
4DPM: Deepfake Detection With a Denoising Diffusion Probabilistic Mask
Yang, R., Deng, Z., Zhang, Y., Luo, X., & Lan, R. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3378127 · Cited by 25 · see citing works
Exposing unseen GAN-generated image using unsupervised domain adaptation
Zhang, M., Wang, H., He, P., Malik, A., & Liu, H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109905 · Cited by 23 · see citing works
Deepfake Detection Based on the Adaptive Fusion of Spatial‐Frequency Features
Wang, F., Chen, Q., Jing, B., Tang, Y., Song, Z., & Wang, B. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/7578036 · Cited by 23 · see citing works
Deepfake Generation and Detection - An Exploratory Study
Garg, D., & Gill, R. (2023) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon59197.2023.10434896 · Cited by 27 · see citing works
- Authors
- Liu, Z., Qi, X., & Torr, P. H. S.
- Affiliations
- University of Oxford (GB)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 8057-8066
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034795015
- Cited by
- 401
- References
- 43
- Citations by year
- 2026: 55 · 2025: 102 · 2024: 71 · 2023: 64 · 2022: 57 · 2021: 42 · 2020: 9 · 2019: 1
- FWCI
- 13.26
- 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)
- The HKU Scholars Hub (University of Hong Kong) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Royal Academy of Engineering
- Multidisciplinary University Research Initiative (EP/N019474/1)
- Engineering and Physical Sciences Research Council (EP/N019474/1, EP/M013774/1, EP/M013774/, ERC-2012-AdG 321162-HELIOS, Seebibyte EP/M013774/1)
- 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
- face forgery detection, deepfake detection, GAN-generated images
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
