The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
FaceForensics++: Learning to Detect Manipulated Facial Images
Open accessAbstract
The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the benchmark is based on Deep-Fakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size. The benchmark is publicly available and contains a hidden test set as well as a database of over 1.8 million manipulated images. This dataset is over an order of magnitude larger than comparable, publicly available, forgery datasets. Based on this data, we performed a thorough analysis of data-driven forgery detectors. We show that the use of additional domain-specific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers.
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In the citing paper
“In this evaluation, only the forgery algorithm is altered. Other factors such as background and identity remain consistent across all the different forgeries.”
From DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection (2023) · cites this paper as [33]
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“FaceForensics++ [51] is a widely used visual-only deepfake dataset containing 5000 videos generated using four distinct deepfake generation methods.”
From Deepfake Media Forensics: Status and Future Challenges (2025) · cites this paper as [51]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“FaceForensics++ [51] is a widely used visual-only deepfake dataset containing 5000 videos generated using four distinct deepfake generation methods.”
From Deepfake Media Forensics: Status and Future Challenges (2025) · cites this paper as [51]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Early deepfake detection methods primarily relied on conventional CNNs to identify low-level visual inconsistencies and blending artifacts in manipulated frames.”
From Cross-dataset video deepfake detection using Transformer and CNN architectures (2026) · cites this paper as [10]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the visual domain, FaceForensics++ (FF++) [36] is the most widely used training dataset, containing over 1,000 videos manipulated with four different techniques: DeepFakes, Face2Face, FaceSwap, and NeuralTextures.”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [36]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Most existing deepfake detection algorithms are based on DNN because of their capability in feature extraction and selection processes (Afchar et al. 2018; Li et al. 2018; Rossler etal. 2019).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Rossler et al. 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Rossler etal. (2019) used a CNN-based method to find content that had been changed. They trained the neural network with a mix of datasets in a supervised way.”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Rossler et al. 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep learning architectures, such as Convolutional Neural Networks (CNNs) and GANs, can find complex and subtle features in deepfake content because they are deep and do not work in a straight line (Rossler etal. 2019).”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Rossler et al. 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“FaceForensics++ introduced a widely used benchmark for facial manipulation detection, providing a standardized evaluation setting across multiple manipulation methods and compression levels [34].”
From A Review of Tools and Technologies to Combat Deepfakes (2026) · cites this paper as [34]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Within the context of Deepfake detection, explainability transcends a simple classification label; It is the comprehensive ability to provide the reasoning for a decision, localize the evidence artifacts, and even shed light on the manipulation methodology [2].”
From From Black Boxes to Glass Boxes: Explainable AI for Trustworthy Deepfake Forensics (2025) · cites this paper as [2]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, the effectiveness of these methods varies, and they often need help to keep up with the rapid advancements in deepfake technology (Rossler et al., 2019).”
From Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media (2024) · cites this paper as Rossler et al., 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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 [36]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The evolution of deepfake datasets has closely followed advances in generative AI, transitioning from early GAN and VAE-based techniques [21], [22], [24], [25] to sophisticated Diffusion Models (DM) [32], [33], [34].”
From Evolving from Single-modal to Multi-modal Facial Deepfake Detection: Progress and Challenges (2024) · cites this paper as [21]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Existing image-level approaches often focus on single frame and ignore the spatiotemporal cues hidden in deepfake videos, resulting in poor generalization and robustness.”
From Deepfake Video Detection with Spatiotemporal Dropout Transformer (2022) · cites this paper as [39]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“It is widely recognized that AI models might be vulnerable to attacks by adversaries with malicious intentions.”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [68]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While these benchmarking methodologies have made significant contributions, they specifically focus on their own datasets, without offering a standardized way to handle data across different datasets”
From DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection (2023) · cites this paper as [33]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“FaceForensics++ (Rossler et al. 2019) is a famous fake video dataset consisting of 1,000 original video sequences that have been manipulated with four automated face manipulation methods: DeepFakes, Face2Face, FaceSwap, and NeuralTextures.”
From Countering Malicious DeepFakes: Survey, Battleground, and Horizon (2022) · cites this paper as Rossler et al. 2019
The cited source says what the claim attributes to it.
In the citing paper
“FF++ [ 10] is one of the most widely used deepfake datasets, containing 1000 pristine videos collected from YouTube and manipulated using four prominent synthesis techniques”
From Cross-dataset video deepfake detection using Transformer and CNN architectures (2026) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“Particularly, in the frame-level setting the DFDT model has achieved 99.41%, 99.31%, and 81.35% on an accuracy score corresponding to FaceForensics++ (raw) [49], Celeb-DF (V2) [55], and WildDeepfake [56], respectively.”
From DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer (2022) · cites this paper as [49]
The cited source says what the claim attributes to it.
In the citing paper
“Visual modality datasets dominate with over 20 publicly available resources [29, 30, 30-44], while audio modality datasets are fewer, with 11 notable datasets [45-55].”
From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [36]
The cited source says what the claim attributes to it.
In the citing paper
“All detectors are trained on FF++ (c23) (Rossler et al., 2019) and tested in cross-domain manner on Celeb-DF (Li et al., 2020b) and different parts of ours VCF dataset.”
From VCF: A Real-World Video Conference Deepfake Benchmark for Face-Swap Detection and Robustness Evaluation (2025) · cites this paper as Rossler et al., 2019
The cited source says what the claim attributes to it.
In the citing paper
“Deepfake detection faces a critical generalization hurdle, with performance deteriorating when there is a mismatch between the distributions of training and testing data.”
From Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection (2024) · cites this paper as [39]
The cited source says what the claim attributes to it.
In the citing paper
“This deep convolutional neural network, known as XceptionNet, has demonstrated high accuracy in detecting deepfake videos.”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Rossler et al. 2019
The cited source says what the claim attributes to it.
In the citing paper
“FaceForensics++ (FF++) (Rössler et al., 2019): This dataset contains 5,000 face videos with over 1.8 million manipulated frames, including 1,000 real videos (with 509,914 frames) downloaded from YouTube, and 4,000 fake videos created using four face manipulation methods (Deepfakes, Face2Face, FaceSwap and NeuralTextures).”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Rössler et al., 2019
The cited source says what the claim attributes to it.
In the citing paper
“The other high-level approaches have generally examined for statistical discrepancies in frame-level visual aspects [10, 13, 26, 30], however, these methods ignore temporal cues in the video.”
From Attention-based Multimodal learning framework for Generalized Audio-Visual Deepfake Detection (2023) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“For instance, CelebA is widely used for image-based deepfake training, while FaceForensics++ is a benchmark in video deepfake detection.”
From Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media (2024) · cites this paper as Rossler et al., 2019
The cited source says what the claim attributes to it.
In the citing paper
“The compressed deepfake videos are generally considered more challenging for detection [57].”
From DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models (2024) · cites this paper as [57]
The cited source says what the claim attributes to it.
In the citing paper
“current benchmarks [37], such as FaceForensics++ [57] and Celeb-DF [36], primarily focus on their own datasets and evaluate various detection methods on these public datasets.”
From DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models (2024) · cites this paper as [57]
The cited source says what the claim attributes to it.
In the citing paper
“FF++ [57] is one of the most popular datasets [22], but it contains limited types of deepfake data.”
From DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models (2024) · cites this paper as [57]
The cited source says what the claim attributes to it.
In the citing paper
“For instance, current studies often directly cite the results of Xception from the original paper [33], but different training settings used in different works can inevitably result in disparities.”
From DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection (2023) · cites this paper as [33]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“It was submitted to the DeepFake Detection Challenge (DFDC), receiving a score of 0.9965 for its AUC-ROC.”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Rossler et al. 2019
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“the existing detection methods generally only evaluate their overall performance on publicly available datasets”
From DEEPFAKER: A Unified Evaluation Platform for Facial Deepfake and Detection Models (2024) · cites this paper as [57]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“many current AI systems are found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection.”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [68]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“In some deepfake datasets ( i.e. , FaceForensics++ [33] and DFD [9]), an additional mask is provided, indicating the regions of the face that are manipulated or modified.”
From DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection (2023) · cites this paper as [33]
Experimental — accuracy not yet measuredIn the citing paper
“Most of them model deepfake detection as a vanilla binary classification problem, i.e, first use a backbone network to extract a global feature and then feed it into a binary classifier (real/fake).”
From Multi-attentional Deepfake Detection (2021) · cites this paper as [34]
In the citing paper
“The FF++ dataset has limits when it comes to spotting lip-sync deepfakes, and some videos might have color discrepancies near the modified faces.”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [118]
In the citing paper
“FaceForensics++ [118] is a well-known dataset used for deepfake detection that was provided in 2019 as an addition to the FaceForensics dataset, which was made available in 2018 and only included videos with altered facial expressions.”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [118]
In the citing paper
“The dataset can be used to test deepfake detection strategies on both compressed and uncompressed videos because it is supplied in two different quality levels.”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [118]
In the citing paper
“It includes 3000 edited videos in addition to 1000 original videos that were pulled from the YouTube-8M dataset.”
From Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions (2023) · cites this paper as [118]
In the citing paper
“It is commonly used to test forgery video detection methods.”
From Deep learning for visual speech analysis: A survey (2024) · cites this paper as [97]
In the citing paper
“Faceforensics++ [97] is an automated benchmark for facial manipulation detection.”
From Deep learning for visual speech analysis: A survey (2024) · cites this paper as [97]
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Exploring Temporal Coherence for More General Video Face Forgery Detection
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Combining EfficientNet and Vision Transformers for Video Deepfake Detection
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Advancing High Fidelity Identity Swapping for Forgery Detection
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Representative Forgery Mining for Fake Face Detection
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A Survey on Deepfake Video Detection
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Deepfake detection by human crowds, machines, and machine-informed crowds
Groh, M., Epstein, Z., Firestone, C., & Picard, R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2110013119 · Cited by 248
Artificial intelligence in digital media: the era of deepfakes
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Detecting Deep-Fake Videos from Appearance and Behavior
Agarwal, S., Farid, H., El-Gaaly, T., & Lim, S.-N. (2020) · 2020 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs49906.2020.9360904 · Cited by 242
“The FaceForensics++ dataset (FF) [27] consists of 1000 YouTube videos of 1000 different people, mostly news anchors”
Deepfake Detection: Current Challenges and Next Steps
Lyu, S. (2020) · 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew46912.2020.9105991 · Cited by 234
Recurrent convolutional structures for audio spoof and video deepfake detection
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DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
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Implicit identity leakage: The stumbling block to improving deepfake detection generalization
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Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
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Joint Audio-Visual Deepfake Detection
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ID-Reveal: Identity-aware DeepFake Video Detection
Cozzolino, D., Rossler, A., Thies, J., Niesner, M., & Verdoliva, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01483 · Cited by 221
Self-citationDeepfake 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
“Face Forensics++ is frequently used in research for deepfake detection and facial forensics (Rossler et al. 2019).”
InfluentialHu, J., Liao, X., Wang, W., & Qin, Z. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3074259 · Cited by 195
Dual Contrastive Learning for General Face Forgery Detection
Sun, K., Yao, T., Chen, S., Ding, S., Li, J., & Ji, R. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20130 · Cited by 209
“To evaluate our method, we conduct experiments on five famous challenging datasets: FaceForen-sics++ (Rossler et al. 2019) is a large-scale forgery face dataset containing 720 videos for training and 280 videos for validation or testing.”
BackgroundMethodAdversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
Hussain, S., Neekhara, P., Jere, M., Koushanfar, F., & McAuley, J. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00339 · Cited by 204
OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder
Khalid, H., & Woo, S. S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00336 · Cited by 197
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Anticipating and Addressing the Ethical Implications of Deepfakes in the Context of Elections
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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
AVoiD-DF: Audio-Visual Joint Learning for Detecting Deepfake
Yang, W., Zhou, X., Chen, Z., Guo, B., Ba, Z., Xia, Z., Cao, X., & Ren, K. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3262148 · Cited by 198
Ilyas, H., Javed, A., & Malik, K. M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110124 · Cited by 186
MTD-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
ISTVT: Interpretable Spatial-Temporal Video Transformer for Deepfake Detection
Zhao, C., Wang, C., Hu, G., Chen, H., Liu, C., & Tang, J. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3239223 · Cited by 184
Deepfake generation and detection: Case study and challenges
Patel, Y., Tanwar, S., Gupta, R., Bhattacharya, P., Davidson, I. E., Nyameko, R., Aluvala, S., & Vimal, V. (2023) · IEEE Access · doi:10.1109/access.2023.3342107 · Cited by 186
Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection
Haliassos, A., Mira, R., Petridis, S., & Pantic, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01453 · Cited by 180
Deepfakes Detection with Automatic Face Weighting
Montserrat, D. M., Hao, H., Yarlagadda, S. K., Baireddy, S., Shao, R., Horvath, J., Bartusiak, E., Yang, J., Guera, D., Zhu, F., et al. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00342 · Cited by 181
Spatiotemporal Inconsistency Learning for DeepFake Video Detection
Gu, Z., Chen, Y., Yao, T., Ding, S., Li, J., Huang, F., & Ma, L. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475508 · Cited by 189
F 2 Trans: High-Frequency Fine-Grained Transformer for Face Forgery Detection
Miao, C., Tan, Z., Chu, Q., Liu, H., Hu, H., & Yu, N. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3233774 · Cited by 172
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
Yan, Z., Luo, Y., Lyu, S., Liu, Q., & Wu, B. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00858 · Cited by 166
“The majority of previous deepfake detectors [29, 37, 39, 40, 57, 59, 63] exhibit effectiveness on the within-dataset scenario, but they often struggle on the cross-dataset scenario where there is a disparity between the distribution of the training and testing data.”
BackgroundMethodInfluentialFake face detection via adaptive manipulation traces extraction network
Guo, Z., Yang, G., Chen, J., & Sun, X. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103170 · Cited by 165
Fooled twice: People cannot detect deepfakes but think they can
Köbis, N. C., Doležalová, B., & Soraperra, I. (2021) · iScience · doi:10.1016/j.isci.2021.103364 · Cited by 193
A Novel Blockchain-Based Deepfake Detection Method Using Federated and Deep Learning Models
Heidari, A., Navimipour, N. J., Dag, H., Talebi, S., & Unal, M. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10255-7 · Cited by 164
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
Deep fake detection and classification using error-level analysis and deep learning
Rafique, R., Gantassi, R., Amin, R., Frnda, J., Mustapha, A., & Alshehri, A. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-34629-3 · Cited by 155
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
Fighting Deepfake by Exposing the Convolutional Traces on Images
Guarnera, L., Giudice, O., & Battiato, S. (2020) · IEEE Access · doi:10.1109/access.2020.3023037 · Cited by 149
High-resolution neural face swapping for visual effects
Naruniec, J., Helminger, L., Schroers, C., & Weber, R. M. (2020) · Computer Graphics Forum · doi:10.1111/cgf.14062 · Cited by 154
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FrePGAN: Robust Deepfake Detection Using Frequency-Level Perturbations
Jeong, Y., Kim, D., Ro, Y., & Choi, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i1.19990 · Cited by 137
CORE: Consistent Representation Learning for Face Forgery Detection
Ni, Y., Meng, D., Yu, C., Quan, C., Ren, D., & Zhao, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00011 · Cited by 136
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
Wang, Y., Chen, X., Zhu, J., Chu, W., Tai, Y., Wang, C., Li, J., Wu, Y., Huang, F., & Ji, R. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/157 · Cited by 138
KoDF: A Large-scale Korean DeepFake Detection Dataset
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DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection
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A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features
Saikia, P., Dholaria, D., Yadav, P., Patel, V., & Roy, M. (2022) · 2022 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892905 · Cited by 133
A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats
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AltFreezing for More General Video Face Forgery Detection
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“All the models are trained on FF++ [42], and tested in FF++ [42], CelebDF v2 [36], and FaceShifter [31] to evaluate the generalization capability of the models.”
BackgroundMethodInfluentialA Robust GAN-Generated Face Detection Method Based on Dual-Color Spaces and an Improved Xception
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Detect and locate: Exposing face manipulation by semantic-and noise-level telltales
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Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
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Optical Flow based CNN for detection of unlearnt deepfake manipulations
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Fighting Deepfakes by Detecting GAN DCT Anomalies
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Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder
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Adversarial Threats to DeepFake Detection: A Practical Perspective
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Face Forgery Detection by 3D Decomposition
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Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
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Deepfakes generation and detection: a short survey
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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
FakeLocator: Robust Localization of GAN-Based Face Manipulations
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Self-Supervised Video Forensics by Audio-Visual Anomaly Detection
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Testing human ability to detect ‘deepfake’ images of human faces
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Audio-Visual Person-of-Interest DeepFake Detection
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Self-citation
Metadata
- Authors
- Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M.
- Affiliations
- University of Naples Federico II (IT)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- Technical University of Munich (DE)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 1-11
- OpenAlex
- W2912336782
- Semantic Scholar
- b4f8c1353aa2d88cacfaef1b3afba74dbf427d89
- Cited by
- 3,314
- References
- 67
- Citations by year
- 2026: 588 · 2025: 735 · 2024: 579 · 2023: 474 · 2022: 406 · 2021: 310 · 2020: 188 · 2019: 33
- FWCI
- 76.37
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (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 › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Keywords
- face manipulation detection, deepfakes, FaceForensics++, benchmark dataset, Face2Face, image compression robustness, human observer comparison, domain knowledge, fake news
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