Two-Stream Neural Networks for Tampered Face Detection
Open accessAbstract
We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream. In addition, we use two different online face swaping applications to create a new dataset that consists of 2010 tampered images, each of which contains a tampered face. We evaluate the proposed two-stream network on our newly collected dataset. Experimental results demonstrate the effectness of our method.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
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 [63]
The SwapMe and FaceSwap dataset contains 4,310 images, including 2,300 real images and 2,010 fake images created using FaceSwap and the SwapMe iOS app.
“SwapMe and FaceSwap dataset (Zhou et al., 2017): This dataset contains 4,310 images, including 2,300 real images and 2,010 fake images created using FaceSwap22 and the SwapMe iOS app (now discontinued).”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Zhou et al., 2017
The approach by Zhou et al. demonstrates superior performance and generalization capabilities compared to unimodal models.
“This approach demonstrates superior performance and generalization capabilities compared to unimodal models.”
From Deepfake forensics: a survey of digital forensic methods for multimodal deepfake identification on social media (2024) · cites this paper as Zhou et al. (2017)
Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267 · see citing works
Rich Models for Steganalysis of Digital Images
Fridrich, J., & Kodovsky, J. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2190402 · Cited by 2,144 · see citing works
Detecting image splicing in the wild (WEB)
Zampoglou, M., Papadopoulos, S., & Kompatsiaris, Y. (2015) · 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2015.7169839 · Cited by 82 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314 · see citing works
Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics
Li, Y., Yang, X., Sun, P., Qi, H., & Lyu, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00327 · Cited by 1,846 · see citing works
“The underlying CNN is the GoogLeNet InceptionV3 model [48] trained on the SwapMe dataset [54].”
MethodInfluentialDeepfake Video Detection Using Recurrent Neural Networks
Guera, D., & Delp, E. J. (2018) · 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639163 · Cited by 1,285 · see citing works
Deepfakes and beyond: A Survey of face manipulation and fake detection
Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.014 · Cited by 1,237 · see citing works
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
Thinking in Frequency: Face Forgery Detection by Mining Frequency-Aware Clues
Qian, Y., Yin, G., Sheng, L., Chen, Z., & Shao, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58610-2_6 · Cited by 1,058 · see citing works
Multi-attentional Deepfake Detection
Zhao, H., Wei, T., Zhou, W., Zhang, W., Chen, D., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00222 · Cited by 1,019 · see citing works
“As the learning based methods become mainstream, some works [53, 34] have proposed frameworks which extract features from spatial domain and have achieved excellent performances on specific datasets.”
BackgroundCapsule-forensics: Using Capsule Networks to Detect Forged Images and Videos
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682602 · Cited by 951 · see citing works
Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations
Matern, F., Riess, C., & Stamminger, M. (2019) · 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw.2019.00020 · Cited by 824 · see citing works
On the Detection of Digital Face Manipulation
Dang, H., Liu, F., Stehouwer, J., Liu, X., & Jain, A. K. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00582 · Cited by 764 · see citing works
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection
Jiang, L., Li, R., Wu, W., Qian, C., & Loy, C. C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00296 · Cited by 599 · see citing works
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
Liu, H., Li, X., Zhou, W., Chen, Y., He, Y., Xue, H., Zhang, W., & Yu, N. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00083 · Cited by 600 · see citing works
Deepfake Detection: A Systematic Literature Review
Rana, M. S., Nobi, M. N., Murali, B., & Sung, A. H. (2022) · IEEE Access · doi:10.1109/access.2022.3154404 · Cited by 573 · see citing works
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection
Haliassos, A., Vougioukas, K., Petridis, S., & Pantic, M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00500 · Cited by 537 · see citing works
Multi-task Learning for Detecting and Segmenting Manipulated Facial Images and Videos
Nguyen, H. H., Fang, F., Yamagishi, J., & Echizen, I. (2019) · 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) · doi:10.1109/btas46853.2019.9185974 · Cited by 549 · see citing works
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
Ciftci, U. A., Demir, I., & Yin, L. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3009287 · Cited by 499 · see citing works
Noiseprint: A CNN-Based Camera Model Fingerprint
Cozzolino, D., & Verdoliva, L. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916364 · Cited by 499 · see citing works
Deep learning for deepfakes creation and detection: A survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103525 · Cited by 464 · see citing works
“[121], and the face tampering detection method twostream NN [122].”
MethodDeep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464
Two-Branch Recurrent Network for Isolating Deepfakes in Videos
Masi, I., Killekar, A., Mascarenhas, R. M., Gurudatt, S. P., & AbdAlmageed, W. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58571-6_39 · Cited by 471 · see citing works
End-to-End Reconstruction-Classification Learning for Face Forgery Detection
Cao, J., Ma, C., Yao, T., Chen, S., Ding, S., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00408 · Cited by 428 · see citing works
Learning Self-Consistency for Deepfake Detection
Zhao, T., Xu, X., Xu, M., Ding, H., Xiong, Y., & Xia, W. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01475 · Cited by 383 · see citing works
M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection
Wang, J., Wu, Z., Ouyang, W., Han, X., Chen, J., Jiang, Y.-G., & Li, S.-N. (2022) · Proceedings of the 2022 International Conference on Multimedia Retrieval · doi:10.1145/3512527.3531415 · Cited by 368 · see citing works
Self-citationWhat Makes Fake Images Detectable? Understanding Properties that Generalize
Chai, L., Bau, D., Lim, S.-N., & Isola, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58574-7_7 · Cited by 357 · see citing works
Emotions Don't Lie: An Audio-Visual Deepfake Detection Method using Affective Cues
Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., & Manocha, D. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413570 · Cited by 319 · see citing works
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Chen, L., Zhang, Y., Song, Y., Liu, L., & Wang, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01815 · Cited by 307 · see citing works
Exploring Temporal Coherence for More General Video Face Forgery Detection
Zheng, Y., Bao, J., Chen, D., Zeng, M., & Wen, F. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01477 · Cited by 320 · 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
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 · see citing works
“detect generic artifacts [7]–[10], while others detect explicit artifacts that result from, for example, image warping [11], image blending [12] and inconsistencies between the image and metadata [13].”
BackgroundDeepfake 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 · see citing works
Detecting Deep-Fake Videos from Phoneme-Viseme Mismatches
Agarwal, S., Farid, H., Fried, O., & Agrawala, M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00338 · Cited by 234 · see citing works
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413707 · Cited by 220 · see citing works
Adversarial 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
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
“…et al., 2018), two-stream neural network with GoogLeNet for observing artifacts and a patch-based triplet network for capturing local noise residual (Zhou et al., 2017), multistream deep learning network for capturing the artifacts by using Face2Face reenactment (Kumar et al., 2020), incorporating…”
MethodDeepfake 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
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.”
BackgroundForgeryNet: 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
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
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
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
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
DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
Yan, Z., Zhang, Y., Yuan, X., Lyu, S., & Wu, B. (2023) · arXiv · doi:10.48550/arxiv.2307.01426 · Cited by 97 · see citing works
“Responding to the risks posed by deepfake contents, numerous deepfake detection methods [53, 22, 33, 32, 52, 3] have been developed to distinguish deepfake contents from real contents, which are generally categorized into three types: naive detector, spatial detector, and frequency detector.”
MethodDFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer
Khormali, A., & Yuan, J.-S. (2022) · Applied Sciences · doi:10.3390/app12062953 · Cited by 88 · see citing works
Detecting DeepFake, FaceSwap and Face2Face facial forgeries using frequency CNN
Kohli, A., & Gupta, A. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10420-8 · Cited by 90 · see citing works
Detecting Deepfake Videos with Temporal Dropout 3DCNN
Zhang, D., Li, C., Lin, F., Zeng, D., & Ge, S. (2021) · Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/178 · Cited by 77 · see citing works
Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM
Han, B., Han, X., Zhang, H., Li, J., & Cao, X. (2021) · IEEE Transactions on Biometrics, Behavior, and Identity Science · doi:10.1109/tbiom.2021.3065735 · Cited by 77 · see citing works
Self-citationiCaps-Dfake: An Integrated Capsule-Based Model for Deepfake Image and Video Detection
Khalil, S. S., Youssef, S. M., & Saleh, S. N. (2021) · Future Internet · doi:10.3390/fi13040093 · Cited by 67 · see citing works
Qureshi, S. M., Saeed, A., Almotiri, S. H., Ahmad, F., & Al Ghamdi, M. A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2037 · Cited by 71 · see citing works
Deepfake: definitions, performance metrics and standards, datasets, and a meta-review
Altuncu, E., Franqueira, V. N. L., & Li, S. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1400024 · Cited by 65 · 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
On Improving Cross-dataset Generalization of Deepfake Detectors
Nadimpalli, A. V., & Rattani, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00019 · Cited by 63 · see citing works
Ramadhani, K. N., Munir, R., & Utama, N. P. (2024) · IEEE Access · doi:10.1109/access.2024.3352890 · Cited by 57 · see citing works
Deepfake Video Detection Based on MesoNet with Preprocessing Module
Xia, Z., Qiao, T., Xu, M., Wu, X., Han, L., & Chen, Y. (2022) · Symmetry · doi:10.3390/sym14050939 · Cited by 56 · see citing works
ADD: Attention-Based DeepFake Detection Approach
Khormali, A., & Yuan, J.-S. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5040049 · Cited by 55 · see citing works
Assessment framework for deepfake detection in real-world situations
Lu, Y., & Ebrahimi, T. (2024) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-024-00621-8 · Cited by 60 · see citing works
“For example, Xray [41] and SBIs [21] meth-ods manually generate blended faces during the training process as fake samples, which reproduce the blending artifacts existing in real-world GAN-synthesized deep-fakes.”
BackgroundDeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
Yan, Z., Zhang, Y., Yuan, X., Lyu, S., & Wu, B. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-0201 · Cited by 98 · 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
Deepfake Video Detection with Spatiotemporal Dropout Transformer
Zhang, D., Lin, F., Hua, Y., Wang, P., Zeng, D., & Ge, S. (2022) · Proceedings of the 30th ACM International Conference on Multimedia · doi:10.1145/3503161.3547913 · Cited by 50 · see citing works
“Combating deepfakes: multi-LSTM and blockchain as proof of authenticity for digital media,”
Ki Chan, C. C., Kumar, V., Delaney, S., & Gochoo, M. (2020) · 2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G) · doi:10.1109/ai4g50087.2020.9311067 · Cited by 48 · see citing works
Watch Those Words: Video Falsification Detection Using Word-Conditioned Facial Motion
Agarwal, S., Hu, L., Ng, E., Darrell, T., Li, H., & Rohrbach, A. (2023) · 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv56688.2023.00469 · Cited by 28 · see citing works
Identity-Driven DeepFake Detection
Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Chen, D., Wen, F., & Guo, B. (2020) · arXiv · doi:10.48550/arxiv.2012.03930 · Cited by 26 · see citing works
Blind detection of glow-based facial forgery
Guo, Z., Hu, L., Xia, M., & Yang, G. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10098-y · Cited by 23 · see citing works
Weakly-supervised deepfake localization in diffusion-generated images
Ţânţaru, D.-C., Oneaţă, E., & Oneaţă, D. (2024) · 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv57701.2024.00614 · Cited by 25 · see citing works
AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection
Zhu, H., Fu, C., Wu, Q., Wu, W., Qian, C., & He, R. (2020) · arXiv · doi:10.48550/arxiv.2011.02674 · Cited by 20 · see citing works
DeepFake detection method based on multi-scale interactive dual-stream network
Cheng, Z., Wang, Y., Wan, Y., & Jiang, C. (2024) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2024.104263 · Cited by 19 · see citing works
DeepFake Detection Algorithms: A Meta-Analysis
Zotov, S., Dremliuga, R., Borshevnikov, A., & Krivosheeva, K. (2020) · 2020 2nd Symposium on Signal Processing Systems · doi:10.1145/3421515.3421532 · Cited by 12 · see citing works
Compressed Deepfake Video Detection Based on 3D Spatiotemporal Trajectories
Chen, Z., Liao, X., Wu, X., & Chen, Y. (2024) · 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc63619.2025.10848944 · Cited by 10 · see citing works
Multisemantic Path Neural Network for Deepfake Detection
Wu, N., Jin, X., Jiang, Q., Wang, P., Zhang, Y., Yao, S., & Zhou, W. (2022) · Security and Communication Networks · doi:10.1155/2022/4976848 · Cited by 9 · see citing works
Attention-based Multimodal learning framework for Generalized Audio-Visual Deepfake Detection
Masood, M., Javed, A., & Irtaza, A. (2023) · Preprint (Research Square) · doi:10.21203/rs.3.rs-3415144/v1 · Cited by 6 · see citing works
Chrominance and Luminance: a study to detect deepfakes
Talib, M. A., Nasir, Q., Nassif, A. B., Fadhl, N. B., & Gouda, O. M. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20618-3 · Cited by 5 · see citing works
- Authors
- Zhou, P., Han, X., Morariu, V. I., & Davis, L. S.
- Affiliations
- University of Maryland, College Park (US)
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1831-1839
- OpenAlex
- W2962958939
- Cited by
- 700
- References
- 4
- Citations by year
- 2026: 42 · 2025: 95 · 2024: 113 · 2023: 109 · 2022: 131 · 2021: 102 · 2020: 75 · 2019: 21 · 2018: 12
- FWCI
- 8.63
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- two-stream network, GoogLeNet, triplet network, camera characteristics, face swapping
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
