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 cross-manipulation evaluation, only the forgery algorithm is altered, while other factors such as background and identity remain consistent across all the different forgeries.
“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]
The database description (§3; page unavailable) says the dataset uses four manipulation methods, but the claim that identity remains consistent across their forgeries conflicts with the paper’s description of those methods. In “Additional information” (page unavailable), the paper says facial reenactment retains the target person’s identity, whereas face swapping replaces the target face with the source face. Thus the forgery algorithm is not the only thing that changes. (LLM, conf=0.85)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredFaceForensics++ is a widely used visual-only deepfake dataset containing 5000 videos generated by four methods.
“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]
Section 3, “Large-Scale Facial Forgery Database” (page unavailable), identifies four manipulation methods and 1,000 pristine videos; the “Face Manipulation Methods” passage reports 4,000 fake videos. Together these support a total of 5,000 videos, but only 4,000 were generated by the four methods. The paper describes a visual forgery dataset, but our copy does not establish that it was already widely used. (LLM, conf=0.88)
Experimental — accuracy not yet measuredFaceForensics++ is a widely used visual-only deepfake dataset containing 5000 videos generated using four distinct deepfake generation methods.
“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]
Section 3 (page not available) identifies four face-manipulation methods and 1,000 pristine videos; the Face Manipulation Methods passage reports 4,000 fake videos. That supports 5,000 videos in total, but not the claim that all 5,000 were generated by the four methods. The passages describe visual facial manipulations but do not establish how widely the dataset is used. (LLM, conf=0.87)
Experimental — accuracy not yet measuredEarly deepfake detection methods primarily relied on conventional CNNs to identify low-level visual inconsistencies and blending artifacts in manipulated frames.
“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]
The Face Manipulation Methods section says some detectors use deep networks to capture subtle low- or high-level inconsistencies, supporting that part of the claim. It does not say early deepfake detectors primarily used conventional CNNs or specifically targeted blending artifacts; its broader multimedia-forensics discussion instead describes early methods as handcrafted-feature approaches and CNN-based work as more recent. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFaceForensics++ (FF++) is the most widely used training dataset, containing over 1,000 videos manipulated with four different techniques: DeepFakes, Face2Face, FaceSwap, and NeuralTextures.
“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]
The Abstract identifies FaceForensics++ as a dataset for training detectors and names all four manipulation methods. The Face Manipulation Methods section reports 4,000 fake videos, supporting “over 1,000.” Our copy does not establish that it is the most widely used training dataset. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Most existing deepfake detection algorithms are based on DNN because of their capability in feature extraction and selection processes.
“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
The Face Manipulation Methods section says more recent forensic literature concentrates on CNN-based solutions, and the Introduction credits CNNs with learning powerful image features. These passages support the prevalence of neural-network detectors and the feature-learning rationale, but they do not establish a capability in feature selection processes. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRossler et al. (2019) used a CNN-based method to find content that had been changed, training the neural network with a mix of datasets in a supervised way.
“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
The Introduction says the authors used CNN features to detect facial manipulations and trained a neural network in a supervised fashion. But it describes one large-scale dataset containing manipulations from four methods, not training with a mix of separate datasets; Section 4.2.2 likewise describes training on all four manipulation methods. (LLM, conf=0.87)
Experimental — accuracy not yet measuredDeep learning architectures such as CNNs and GANs can find complex and subtle features in deepfake content because they are deep and nonlinear.
“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
The Introduction (page not provided) says CNNs learn powerful image features for detecting facial manipulations, and Section 4.2.2 reports that larger-capacity networks perform better than shallow CNNs on compressed video. The paper discusses GANs as tools for generating manipulated faces, not as detectors that find subtle features. It also does not attribute detection ability specifically to network depth and nonlinearity. (LLM, conf=0.88)
Experimental — accuracy not yet measuredFaceForensics++ introduced a widely used benchmark for facial manipulation detection, providing a standardized evaluation setting across multiple manipulation methods and compression levels.
“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]
The Introduction (Section 1) describes an automated benchmark for standardized comparison across four manipulation methods under random compression, and Section 5 describes its evaluation setting. The Discussion (Section 6) says the benchmark was publicly available and already used by other researchers, but our copy does not establish that it was widely used. (LLM, conf=0.83)
Experimental — accuracy not yet measuredExplainability in deepfake detection requires providing reasoning for a decision, localizing evidence artifacts, and illuminating manipulation methodology.
“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]
Appendix C (Classification of Manipulation Method; page unavailable) reports a classifier that distinguishes four manipulation methods from pristine images, supporting the possibility of identifying the method used. The Face Manipulation Methods section discusses detectable artifacts, but the paper does not define explainability as providing reasons for a decision or localizing the artifacts that justify it. (LLM, conf=0.79)
Experimental — accuracy not yet measuredThe effectiveness of digital forensic methods varies, and they often need help to keep up with the rapid advancements in deepfake technology.
“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
The Face Manipulation Methods section says detection approaches show impressive results but often leave robustness unaddressed, and the Forgery Detection of GAN-based methods section reports lower accuracy for one manipulation method. These passages support varying effectiveness, but they do not say forensic methods struggle to keep pace with rapid advances in deepfake technology. Page numbers were not available in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredExisting 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 [36]
The Benchmark section reports lower detector performance when the test scenario differed from the training database. The Face Manipulation Methods section also says robustness issues often remain unaddressed. These passages support a generalization concern, but they do not establish dramatic drops across unforeseen domains generally; the measured drop concerns a benchmark with randomized quality levels and possible tracking errors. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe evolution of deepfake datasets has closely followed advances in generative AI, transitioning from early GAN and VAE-based techniques to sophisticated Diffusion Models.
“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]
The Large-Scale Facial Forgery Database section describes an early deepfake dataset containing DeepFakes and NeuralTextures. The DeepFakes section identifies an autoencoder-based method, and the NeuralTextures description identifies an adversarial (GAN) loss. The paper does not identify its autoencoders as VAEs; the later diffusion-model portion of the citing sentence is attached to other citations, not this source. Page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredExisting image-level deepfake detection approaches often focus on single frames and ignore spatiotemporal cues hidden in deepfake videos, resulting in poor generalization and robustness.
“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]
The Face Manipulation Methods section describes image-based cues used by facial-forgery detectors and says robustness issues often remain unaddressed. The detection results also show performance drops on compressed videos. But the paper does not attribute poor generalization or robustness to ignoring spatiotemporal cues; it emphasizes compression and resizing as challenges instead (page numbers unavailable in our copy). (LLM, conf=0.85)
Experimental — accuracy not yet measuredAI models might be vulnerable to attacks by adversaries with malicious intentions.
“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]
The Face Manipulation Methods section says forgery detectors have robustness issues and that compression and resizing can erase traces they use to detect manipulation. That supports vulnerability in these particular models, but the paper does not establish the broader claim that AI models are vulnerable to intentional attacks by malicious adversaries. (LLM, conf=0.78)
Experimental — accuracy not yet measuredExisting deepfake benchmarks focus on their own datasets, without offering a standardized way to handle data across different datasets.
“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]
Section 5 (Benchmark; page unavailable) describes a benchmark built from additional videos collected and manipulated for FaceForensics++, supporting the point that this benchmark uses its own data. It does not establish that existing benchmarks generally focus only on their own datasets or lack a standardized way to handle data across datasets; its stated goal is standardized comparison of follow-up work. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFaceForensics++ consists of 1,000 original video sequences manipulated with four automated face manipulation methods: DeepFakes, Face2Face, FaceSwap, and NeuralTextures.
“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
FF++ contains 1000 pristine videos collected from YouTube and manipulated using four prominent synthesis techniques.
“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]
In frame-level setting, DFDT achieved accuracy of 99.41% on FaceForensics++ (raw), 99.31% on Celeb-DF (V2), and 81.35% on WildDeepfake.
“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]
Visual modality datasets dominate with over 20 publicly available resources, while audio modality datasets are fewer, with 11 notable datasets.
“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]
All detectors are trained on FF++ (c23) and tested in a cross-domain manner on Celeb-DF and different parts of the VCF dataset.
“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
Most previous deepfake detectors perform well within-dataset but struggle on cross-dataset scenarios where training and testing data distributions differ.
“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]
XceptionNet, a deep convolutional neural network, has demonstrated high accuracy in detecting deepfake videos.
“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
FaceForensics++ (FF++) contains 5,000 face videos with over 1.8 million manipulated frames, including 1,000 real YouTube videos and 4,000 fake videos created using four face manipulation methods.
“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
High-level deepfake detection approaches examine statistical discrepancies in frame-level visual aspects but ignore temporal cues.
“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]
FaceForensics++ is a benchmark in video deepfake detection.
“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
Compressed deepfake videos are generally considered more challenging for detection.
“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]
Current benchmarks primarily focus on their own datasets and evaluate various detection methods on these public datasets.
“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]
FF++ is one of the most popular deepfake datasets but contains limited types of deepfake data.
“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]
XceptionNet received an AUC-ROC score of 0.9965 in the DeepFake Detection Challenge (DFDC).
“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
Section 4.2.2 describes the XceptionNet detector, and Section 5 reports results on the paper’s own benchmark. Searches for DFDC and 0.9965 AUC-ROC found no passage in our copy reporting a DFDC submission or that score. (LLM, conf=0.78)
Experimental — accuracy not yet measuredExisting detection methods generally only evaluate their overall performance on publicly available datasets.
“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]
Section 4.2.2 reports the authors' evaluation of detection methods on their dataset, and Section 5 describes their public benchmark. Neither passage says that existing methods generally evaluate only overall performance on publicly available datasets. Searches for prior evaluation practices and that exclusivity claim found no supporting passage in our copy. (LLM, conf=0.70)
Experimental — accuracy not yet measuredMany current AI systems are vulnerable to imperceptible attacks.
“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]
The Face Manipulation Methods section discusses robustness of facial-forgery detectors to compression and resizing, and §4.2.2 reports lower detection performance on compressed videos. Searches for imperceptible adversarial attacks and perturbations found no passage saying that many current AI systems are vulnerable to such attacks. (LLM, conf=0.74)
Experimental — accuracy not yet measuredIn some deepfake datasets, specifically FaceForensics++ and DFD, an additional mask is provided indicating the regions of the face that are manipulated or modified.
“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]
The Abstract identifies FaceForensics++ as a dataset of facial forgeries, but the passages in our copy do not say that it provides masks marking manipulated regions. Searches for “mask,” “masks,” and localization ground truth found no passage establishing that feature. DFD has a separate citation and is not assessed here. (LLM, conf=0.73)
Experimental — accuracy not yet measuredMost existing deepfake detection methods model the problem as a vanilla binary classification (real/fake), using a backbone to extract global features and a binary classifier.
“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]
FF++ has limits for spotting lip-sync deepfakes and some videos may have color discrepancies near modified faces.
“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]
FaceForensics++ 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.
“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]
FF++ is supplied in two quality levels and can test deepfake detection on compressed and uncompressed videos.
“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]
FF++ includes 3000 edited videos and 1000 original videos pulled from YouTube-8M.
“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]
Faceforensics++ is commonly used to test forgery video detection methods.
“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]
Faceforensics++ is an automated benchmark for facial manipulation detection.
“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]
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.
“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]
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Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
Chugh, K., Gupta, P., Dhall, A., & Subramanian, R. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413700 · Cited by 225 · see citing works
Joint Audio-Visual Deepfake Detection
Zhou, Y., & Lim, S.-N. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01453 · Cited by 217 · see citing works
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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., & Wei, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28310 · Cited by 215 · see citing works
Anticipating and Addressing the Ethical Implications of Deepfakes in the Context of Elections
Diakopoulos, N., & Johnson, D. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3474183 · Cited by 194 · 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
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 · see citing works
Ilyas, H., Javed, A., & Malik, K. M. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110124 · Cited by 186 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
F <sup>2</sup> 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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
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 · 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
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 · 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
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 · see citing works
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 · see citing works
Wang, Y., Yu, K., Chen, C., Hu, X., & Peng, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00703 · Cited by 161 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
KoDF: A Large-scale Korean DeepFake Detection Dataset
Kwon, P., You, J., Nam, G., Park, S., & Chae, G. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01057 · Cited by 145 · see citing works
DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection
Rana, M. S., & Sung, A. H. (2020) · 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) · doi:10.1109/cscloud-edgecom49738.2020.00021 · Cited by 133 · see citing works
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 · see citing works
A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats
Mubarak, R., Alsboui, T., Alshaikh, O., Inuwa-Dutse, I., Khan, S., & Parkinson, S. (2023) · IEEE Access · doi:10.1109/access.2023.3344653 · Cited by 132 · see citing works
AltFreezing for More General Video Face Forgery Detection
Wang, Z., Bao, J., Zhou, W., Wang, W., & Li, H. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00402 · Cited by 137 · see citing works
“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
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
Detect and locate: Exposing face manipulation by semantic-and noise-level telltales
Kong, C., Chen, B., Li, H., Wang, S., Rocha, A., & Kwong, S. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3169921 · Cited by 120 · see citing works
Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
Naitali, A., Ridouani, M., Salahdine, F., & Kaabouch, N. (2023) · Computers · doi:10.3390/computers12100216 · Cited by 117 · see citing works
Optical Flow based CNN for detection of unlearnt deepfake manipulations
Caldelli, R., Galteri, L., Amerini, I., & Del Bimbo, A. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.005 · Cited by 115 · see citing works
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
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder
Du, M., Pentyala, S., Li, Y., & Hu, X. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3411892 · Cited by 114 · see citing works
Adversarial Threats to DeepFake Detection: A Practical Perspective
Neekhara, P., Dolhansky, B., Bitton, J., & Ferrer, C. C. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00103 · Cited by 117 · 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
Interpretable and Trustworthy Deepfake Detection via Dynamic Prototypes
Trinh, L., Tsang, M., Rambhatla, S., & Liu, Y. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00202 · Cited by 115 · see citing works
Deepfakes generation and detection: a short survey
Akhtar, Z. (2023) · Journal of Imaging · doi:10.3390/jimaging9010018 · Cited by 106 · see citing works
Das, S., Seferbekov, S., Datta, A., Islam, M. S., & Amin, M. R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) · doi:10.1109/iccvw54120.2021.00421 · Cited by 107 · see citing works
Deepfake Media Forensics: Status and Future Challenges
Amerini, I., Barni, M., Battiato, S., Bestagini, P., Boato, G., Bruni, V., Caldelli, R., De Natale, F., De Nicola, R., Guarnera, L., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11030073 · Cited by 113 · see citing works
FakeLocator: Robust Localization of GAN-Based Face Manipulations
Huang, Y., Juefei-Xu, F., Guo, Q., Liu, Y., & Pu, G. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3141262 · Cited by 102 · see citing works
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
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
Audio-Visual Person-of-Interest DeepFake Detection
Cozzolino, D., Pianese, A., Nießner, M., & Verdoliva, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00101 · Cited by 104 · see citing works
Self-citation
- 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 · 2018: 1
- 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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