Deepfake Detection: Current Challenges and Next Steps
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High quality fake videos and audios generated by AI- algorithms (the deep fakes) have started to challenge the status of videos and audios as definitive evidence of events. In this paper, we highlight a few of these challenges and discuss the research opportunities in this direction.
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The authors of many surveys argued that better (higher-quality, more up-to-date, and more standard) deepfake datasets are needed to develop more effective deepfake detection methods.
“The authors of many surveys (e.g., Lyu, 2020; Younus and Hasan, 2020; Deshmukh and Wankhade, 2021; Masood et al., 2023; Sandotra and Arora, 2024), argued that better (higher-quality, more up-to-date, and more standard) deepfake datasets are needed to develop more effective deepfake detection methods.”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Lyu, 2020
Section 2.1, “Limitations,” argues that existing deepfake datasets contain low-quality artifacts that can make detection performance less relevant in real-world use; it also reports poor transfer between datasets. This supports the need for higher-quality datasets, but the paper does not specifically call for more up-to-date or standardized datasets. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCurrent DeepFake generation methods cannot produce a fine mapping of color shades for hair with respect to the human face.
“The author tried to raise a deep concern about one of the critical disadvantages of the current DeepFake generation methods that cannot produce a fine mapping of color shades for hair with respect to the human face.”
From A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods (2023) · cites this paper as [19]
The Conclusion (page unavailable) calls the inability to produce good details, including facial hair, a critical disadvantage of current DeepFake generation methods. The Limitations section also notes color mismatch in videos from existing datasets. Neither passage says the methods cannot finely map hair color shades relative to the face. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLyu (2020) and Masood et al. (2023) suggested that social media laundering effects in training data need to be considered and the evaluation of datasets improved.
“Lyu (2020) and Masood et al. (2023) also suggested that we need to consider social media laundering effects in training data and improve the evaluation of datasets.”
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Lyu, 2020
Some authors of the 15 survey papers used a broader definition of deepfake, e.g., Lyu (2020) defined it as "high quality fake videos and audios generated by AI algorithms."
“some used a broader definition, e.g., Lyu (2020) defined deepfake as “high quality fake videos and audios generated by AI algorithms.””
From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Lyu, 2020
Traditional digital media forensics often rely on image metadata that are not available for many of today's most concerning deepfakes, which typically appear first on social media platforms stripped of such metadata.
“traditional digital media forensics, which often rely on image metadata (14) that are not available for many of today’s most concerning deepfakes, which typically appear first on social media platforms stripped of such metadata (15, 16).”
From Deepfake detection by human crowds, machines, and machine-informed crowds (2022) · cites this paper as [16]
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Cites
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2017) · arXiv · doi:10.48550/arxiv.1710.10196 · Cited by 4,727 · see citing works
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
MesoNet: a Compact Facial Video Forgery Detection Network
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630761 · Cited by 1,872 · 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 · cited 2× · see citing works
“In a recent work [27], we present a new large-scale challenging DeepFake video dataset, Celeb-DF, which contains 5, 639 high-quality DeepFake videos of celebrities generated using improved synthesis process.”
BackgroundMethodSelf-citationDeepfake 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
Exposing Deep Fakes Using Inconsistent Head Poses
Yang, X., Li, Y., & Lyu, S. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8683164 · Cited by 1,196 · see citing works
Self-citationIn Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking
Li, Y., Chang, M.-C., & Lyu, S. (2018) · 2018 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs.2018.8630787 · Cited by 1,089 · see citing works
Self-citationCapsule-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
Deep Fakes: A Looming Challenge for Privacy, Democracy, and National Security
Chesney, R., & Citron, D. K. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3213954 · Cited by 880 · cited 1× · see citing works
“Well-crafted DeepFake videos can create illusions of a person’s presence and activities that do not occur in reality, which can lead to serious political, social, financial, and legal consequences [7].”
BackgroundLearning Rich Features for Image Manipulation Detection
Zhou, P., Han, X., Morariu, V. I., & Davis, L. S. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00116 · Cited by 837 · 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
Exposing DeepFake Videos By Detecting Face Warping Artifacts
Li, Y., & Lyu, S. (2018) · arXiv · doi:10.48550/arxiv.1811.00656 · Cited by 785 · see citing works
Self-citationTwo-Stream Neural Networks for Tampered Face Detection
Zhou, P., Han, X., Morariu, V. I., & Davis, L. S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.229 · Cited by 700 · 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
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
Korshunov, P., & Marcel, S. (2018) · arXiv · doi:10.48550/arxiv.1812.08685 · Cited by 497 · see citing works
Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries
Bappy, J. H., Simons, C., Nataraj, L., Manjunath, B. S., & Roy–Chowdhury, A. K. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2895466 · Cited by 483 · see citing works
Recurrent convolutional strategies for face manipulation detection in videos
Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., & Natarajan, P. (2019) · arXiv · doi:10.48550/arxiv.1905.00582 · Cited by 443 · see citing works
(2019) · Cited by 415 · see citing works
The Deepfake Detection Challenge (DFDC) Preview Dataset
Dolhansky, B., Howes, R., Pflaum, B., Baram, N., & Ferrer, C. C. (2019) · arXiv · doi:10.48550/arxiv.1910.08854 · Cited by 413 · see citing works
Deep voice 3: scaling text-to-speech with convolutional sequence learning
Ping, W., Peng, K., Gibiansky, A., Arik, S. O., Kannan, A., Narang, S., Raiman, J., & Miller, J. (2017) · arXiv · doi:10.48550/arxiv.1710.07654 · Cited by 313 · see citing works
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye BlinkingPreprint
Li, Y., Chang, M., & Lyu, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.02877 · Cited by 176 · see citing works
Self-citationUse of a Capsule Network to Detect Fake Images and VideosPreprint
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.12467 · Cited by 163 · see citing works
Image Forgery Localization based on Multi-Scale Convolutional Neural Networks
Liu, Y., Guan, Q., Zhao, X., & Cao, Y. (2018) · ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) · doi:10.1145/3206004.3206010 · Cited by 106 · see citing works
Farid, H. (2008) · Scientific American · doi:10.1038/scientificamerican0608-66 · Cited by 77 · see citing works
Detecting AI-Synthesized Speech Using Bispectral Analysis
AlBadawy, E. A., Lyu, S., & Farid, H. (2019) · Cited by 36 · see citing works
Self-citationHiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial PerturbationsPreprint
Li, Y., Yang, X., Wu, B., & Lyu, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.09288 · Cited by 20 · see citing works
Self-citationGu, Y., & Kang, Y. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1506 · Cited by 16 · see citing works
Recurrent-Convolution Approach to DeepFake Detection - State-Of-Art Results on FaceForensics++Preprint
Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., & Natarajan, P. (2019) · arXiv (Cornell University) · Cited by 5 · see citing works
Flagged first, then by citations
4 of the 34 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Falko Matern, Christian Riess, and Marc Stamminger, "Ex- ploiting visual artifacts to expose deepfakes and face manip- ulations," in IEEE Winter Applications of Computer Vision Workshops (WACVW), 2019.
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio, "Generative adversarial nets," in Advances in Neural Information Processing Systems (NIPS), 2014, pp. 2672-2680.
Cited by
Sorted by citations
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
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. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464
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
“Thus far, there have been numerous methods proposed to detect deepfakes [19]–[23].”
MethodEnd-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
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 · see citing works
Deepfake video detection: challenges and opportunities
Kaur, A., Noori Hoshyar, A., Saikrishna, V., Firmin, S., & Xia, F. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10810-6 · Cited by 214 · see citing works
“Researchers have developed wide-range deepfake detection methods using various factors (Lyu 2020).”
Hu, 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
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
“In addition, there are also a few relatable DeepFake surveys first published in late 2019 and early 2020 (Nguyen et al., 2019d; Tolosana et al., 2020; Lyu, 2020).”
BackgroundA Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
Almutairi, Z., & Elgibreen, H. (2022) · Algorithms · doi:10.3390/a15050155 · Cited by 187 · see citing works
TweepFake: about detecting deepfake tweets
Fagni, T., Falchi, F., Gambini, M., Martella, A., & Tesconi, M. (2021) · PLOS ONE · doi:10.1371/journal.pone.0251415 · Cited by 185 · see citing works
Anti-Forensics for Face Swapping Videos via Adversarial Training
Ding, F., Zhu, G., -, Y. L., Zhang, X., Atrey, P. K., & Lyu, S. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3098422 · Cited by 146 · see citing works
Self-citationCybersecurity in the AI-Based Metaverse: A Survey
Pooyandeh, M., Han, K. J., & Sohn, I. (2022) · Applied Sciences · doi:10.3390/app122412993 · Cited by 134 · see citing works
Deep Learning for Deepfakes Creation and Detection: A Survey
Nguyen, T. T., Nguyen, M. C., Nguyen, D. T., Nguyen, D. T., & Nahavandi, S. (2020) · Figshare · doi:10.6084/m9.figshare.12731039.v1 · Cited by 122 · see citing works
Fusing Global and Local Features for Generalized AI-Synthesized Image Detection
Ju, Y., Jia, S., Ke, L., Xue, H., Nagano, K., & Lyu, S. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897820 · Cited by 103 · see citing works
Self-citationPreventing DeepFake Attacks on Speaker Authentication by Dynamic Lip Movement Analysis
Yang, C.-Z., Ma, J., Wang, S., & Liew, A. W. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3045937 · Cited by 99 · 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
A Comprehensive Review of DeepFake Detection Using Advanced Machine Learning and Fusion Methods
Gupta, G., Raja, K., Gupta, M., Jan, T., Whiteside, S. T., & Prasad, M. (2023) · Electronics · doi:10.3390/electronics13010095 · Cited by 99 · see citing works
Information and Media Literacy in the Age of AI: Options for the Future
Tiernan, P., Costello, E., Donlon, E., Parysz, M., & Scriney, M. (2023) · Education Sciences · doi:10.3390/educsci13090906 · Cited by 76 · see citing works
Human detection of political speech deepfakes across transcripts, audio, and video
Groh, M., Sankaranarayanan, A., Singh, N., Kim, D. Y., Lippman, A., & Picard, R. (2024) · Nature Communications · doi:10.1038/s41467-024-51998-z · Cited by 89 · see citing works
Proactive Deepfake Defence via Identity Watermarking
Zhao, Y., Liu, B., Ding, M., Liu, B., Zhu, T., & Yu, X. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00458 · Cited by 71 · see citing works
Shaaban, O. A., Yıldırım, R., & Alguttar, A. A. (2023) · IEEE Access · doi:10.1109/access.2023.3333866 · Cited by 80 · see citing works
Deepfake Video Detection Based on EfficientNet-V2 Network
Deng, L., Suo, H., & Li, D. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3441549 · Cited by 72 · see citing works
Shin, S. Y., & Lee, J. (2022) · Digital Journalism · doi:10.1080/21670811.2022.2026797 · Cited by 67 · see citing works
TIMIT-TTS: A Text-to-Speech Dataset for Multimodal Synthetic Media Detection
Salvi, D., Hosler, B., Bestagini, P., Stamm, M. C., & Tubaro, S. (2023) · IEEE Access · doi:10.1109/access.2023.3276480 · Cited by 65 · 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
Human performance in detecting deepfakes: A systematic review and meta-analysis of 56 papers
Diel, A., Lalgi, T., Schröter, I. C., MacDorman, K. F., Teufel, M., & Bäuerle, A. (2024) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2024.100538 · Cited by 64 · see citing works
El‐Shafai, W., Fouda, M. A., El‐Rabaie, E. M., & Abdel-Salam, N. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15609-1 · Cited by 72 · see citing works
An Investigation of the Effectiveness of Deepfake Models and Tools
Mukta, M. S. H., Ahmad, J., Raiaan, M. A. K., Islam, S., Azam, S., Ali, M. E., & Jonkman, M. (2023) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan12040061 · Cited by 71 · see citing works
An Analytical Perspective on Various Deep Learning Techniques forDeep Fake Detection
Kosarkar, U. P., Sakarkar, G., & Gedam, S. (2022) · International Journal of Innovations in Engineering and Science · doi:10.46335/ijies.2022.7.8.5 · Cited by 61 · see citing works
GLFF: Global and Local Feature Fusion for AI-Synthesized Image Detection
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Khoo, B., Phan, R. C. ‐., & Lim, C. H. (2021) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1438 · Cited by 56 · see citing works
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Sharma, V. K., Garg, R. K., & Caudron, Q. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19906-1 · Cited by 61 · see citing works
NVC-Net: End-To-End Adversarial Voice Conversion
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Cross, C. (2022) · Crime Prevention & Community Safety · doi:10.1057/s41300-021-00134-w · Cited by 39 · see citing works
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Almutairi, Z. M., & ElGibreen, H. (2023) · IEEE Access · doi:10.1109/access.2023.3286864 · Cited by 46 · see citing works
“He looks very real”: Media, knowledge, and search‐based strategies for deepfake identification
Goh, D. H. (2024) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24867 · Cited by 48 · see citing works
An integrated spatiotemporal-based methodology for deepfake detection
Ismail, A., Elpeltagy, M. S., Zaki, M. S., & ElDahshan, K. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07633-3 · Cited by 46 · see citing works
Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning
Ram, R. S., Kumar, M. V., Al-shami, T. M., Masud, M., Aljuaid, H., & Abouhawwash, M. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.030486 · Cited by 39 · see citing works
A Review of Deepfake Technology: An Emerging AI Threat
Sharma, M., & Kaur, M. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-5301-8_44 · Cited by 37 · see citing works
Video and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve
Akhtar, Z., Pendyala, T. L., & Athmakuri, V. S. (2024) · Forensic Sciences · doi:10.3390/forensicsci4030021 · Cited by 40 · see citing works
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Tipper, S., Atlam, H. F., & Lallie, H. S. (2024) · Applied Sciences · doi:10.3390/app14219754 · Cited by 41 · see citing works
Kaate, I., Salminen, J. O., Santos, J. M., Jung, S., Olkkonen, R., & Jansen, B. J. J. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103096 · Cited by 35 · see citing works
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Gil, R., Virgili‐Gomà, J., López, J. M., & Garcı́a, R. (2023) · Soft Computing · doi:10.1007/s00500-023-08605-y · Cited by 40 · see citing works
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Shahzad, S. A., Hashmi, A., Khan, S., Peng, Y.-T., Tsao, Y., & Wang, H.-M. (2022) · 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9980296 · Cited by 33 · see citing works
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Jia, S., Li, X., & Lyu, S. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897972 · Cited by 36 · see citing works
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Hasan, H. R., Salah, K., Jayaraman, R., Yaqoob, I., & Omar, M. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101133 · Cited by 30 · see citing works
A Qualitative Survey on Deep Learning Based Deep fake Video Creation and Detection Method
Rahman, A., Islam, M. S., Moon, M. J., Tasnim, T., Siddique, N., (19227628), M. S., & (9745510), S. A. (2022) · Australian Journal of Engineering and Innovative Technology · doi:10.34104/ajeit.022.013026 · Cited by 30 · see citing works
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Lomnitz, M., Hampel-Arias, Z., Sandesara, V., & Hu, S. (2020) · 2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) · doi:10.1109/aipr50011.2020.9425192 · Cited by 30 · see citing works
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Cao, J., Zhang, K.-Y., Yao, T., Ding, S., Yang, X., & Ma, C. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02151-2 · Cited by 27 · see citing works
Narayan, K., Agarwal, H., Thakral, K., Mittal, S., Vatsa, M., & Singh, R. (2022) · 2022 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb54206.2022.10007968 · Cited by 26 · see citing works
Artificial Intelligence into Multimedia Deepfakes Creation and Detection
Khder, M. A., Shorman, S., Aldoseri, D. T., & Saeed, M. M. (2023) · 2023 International Conference on IT Innovation and Knowledge Discovery (ITIKD) · doi:10.1109/itikd56332.2023.10099744 · Cited by 27 · see citing works
Cai, Z., & Li, M. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101597 · Cited by 26 · see citing works
Visual-Semantic Transformer for Face Forgery Detection
Xu, Y., Jia, G., Huang, H., Duan, J., & He, R. (2021) · 2021 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb52358.2021.9484407 · Cited by 24 · see citing works
Audio Deepfake Detection Using Deep Learning
Anagha, R., Arya, A., Narayan, V. H., Abhishek, S., & Anjali, T. (2023) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart59791.2023.10428163 · Cited by 24 · see citing works
The spread of synthetic media on X
Corsi, G., Marino, B., & Wong, W. (2024) · Harvard Kennedy School Misinformation Review · doi:10.37016/mr-2020-140 · Cited by 24 · see citing works
Video authentication detection using deep learning: a systematic literature review
Alrawahneh, A. A.-M., Abdullah, S. N. H. S., Abdullah, S. N. H. S., Kamarudin, N. H., & Taylor, S. K. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05997-8 · Cited by 24 · see citing works
Forgery-Domain-Supervised Deepfake Detection With Non-Negative Constraint
Yuan, Y., Fu, X., Wang, G., Li, Q., & Li, X. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3193590 · Cited by 25 · see citing works
Fake Speech Recognition Using Deep Learning
Camacho, S., Ballesteros, D. M., & Renza, D. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-86702-7_4 · Cited by 22 · see citing works
Kaate, I., Salminen, J. O., Jung, S., Almerekhi, H., & Jansen, B. J. J. (2023) · Biannual Conference of the Italian SIGCHI Chapter (CHItaly) · doi:10.1145/3605390.3605397 · Cited by 22 · see citing works
Comparative study of deep learning techniques for DeepFake video detection
Khan, R., Sohail, M., Usman, I., Sandhu, M. M., Jafri, M. R., Yaqub, M. A., & Liotta, A. (2024) · ICT Express · doi:10.1016/j.icte.2024.09.018 · Cited by 23 · see citing works
Salam, M. A., Rayun, S. M. N., Islam, W., Hasan, R., Firmansyah, E. A., & Kalinaki, K. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-5298-4.ch020 · Cited by 20 · see citing works
Hybrid Recurrent Deep Learning Model for DeepFake Video Detection
Jaiswal, G. (2021) · 2021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon52273.2021.9667632 · Cited by 20 · see citing works
Deepfakes in digital media forensics: Generation, AI-based detection and challenges
Bendiab, G., Haiouni, H., Moulas, I., & Shiaeles, S. N. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103935 · Cited by 22 · see citing works
Kaate, I., Salminen, J. O., Santos, J. M., Jung, S., Almerekhi, H., & Jansen, B. J. J. (2023) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2023.100031 · Cited by 19 · see citing works
Extending the theory of information poverty to deepfake technology
Matli, W. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100286 · Cited by 19 · see citing works
Gilbert, C., & Gilbert, M. A. (2024) · International Journal of Scientific Research and Modern Technology. · doi:10.38124/ijsrmt.v3i10.54 · Cited by 22 · see citing works
Audio Deepfake Detection Using Deep Learning
Shaaban, O. A., & Yıldırım, R. (2025) · Engineering Reports · doi:10.1002/eng2.70087 · Cited by 19 · see citing works
Zhang, K., Hua, Z., Zhang, Y., Guo, Y., & Xiang, T. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3520001 · Cited by 17 · see citing works
Sütterlin, S., Ask, T. F., Mägerle, S., Glöckler, S., Wolf, L., Schray, J., Chandi, A., Bursac, T., Khodabakhsh, A., Knox, B. J., et al. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-35017-7_18 · Cited by 16 · see citing works
Detecting Digital Deception: A CNN-RNN hybrid Approach of Deepfake Detection
Singh, H., Kumar, R., Gupta, M., & Chilluri, V. S. B. (2025) · International Conference on Pervasive Computational Technologies (ICPCT) · doi:10.1109/icpct64145.2025.10940830 · Cited by 16 · see citing works
Deepfake Detection: A Multi-Algorithmic and Multi-Modal Approach for Robust Detection and Analysis
Nailwal, S., Singhal, S., Singh, N. T., & Raza, A. (2023) · 2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) · doi:10.1109/rmkmate59243.2023.10369155 · Cited by 15 · see citing works
DeepFake Detection using 3D-Xception Net with Discrete Fourier Transformation
Biswas, A., Bhattacharya, D., & Kumar, K. A. (2021) · Journal of Information Systems and Telecommunication (JIST) · doi:10.52547/jist.9.35.161 · Cited by 14 · see citing works
Deepfake Detection in Video and Audio Clips: A Comprehensive Survey and Analysis
Jbara, W. A., Hussein, N. A.-H. K., & Soud, J. H. (2024) · Mesopotamian Journal of CyberSecurity · doi:10.58496/mjcs/2024/025 · Cited by 16 · see citing works
Deep learning based DeepFake video detection
Guefrachi, S., Jabra, M. B., Alsharabi, N., Othman, M. T. B., Alharabi, Y. O., Alkholidi, A., & Hammam, H. (2023) · International Conference on Smart Computing and Application (ICSCA) · doi:10.1109/icsca57840.2023.10087584 · Cited by 14 · see citing works
Style Equalization: Unsupervised Learning of Controllable Generative Sequence ModelsPreprint
Chang, J.-H. R., Shrivastava, A., Koppula, H. S., Zhang, X., & Tuzel, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02891 · Cited by 15 · see citing works
Deepfake defense: Combining spatial and temporal cues with CNN–BiLSTM–transformer architecture
Yadav, S., & Mangalampalli, S. S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0334980 · Cited by 12 · see citing works
A Visually Interpretable Forensic Deepfake Detection Tool Using Anchors
Jayakumar, K., & Skandhakumar, N. (2022) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr57877.2022.9993294 · Cited by 13 · see citing works
Comparative Analysis and Evaluation of CNN Models for Deepfake Detection
Ritter, P., Lucian, D., Anderies, & Chowanda, A. (2023) · International Conference on Artificial Intelligence and Data Sciences (AiDAS) · doi:10.1109/aidas60501.2023.10284611 · Cited by 13 · see citing works
Tsinganos, N., Fouliras, P., Mavridis, I. K., & Gritzalis, D. A. (2024) · IEEE Access · doi:10.1109/access.2024.3359030 · Cited by 13 · see citing works
Analysis of the current state of deepfake techniques-creation and detection methods
Abu-Ein, A. A.-K. H., Al-Hazaimeh, O. M., Dawood, A. M., & Swidan, A. I. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v28.i3.pp1659-1667 · Cited by 13 · see citing works
Bommareddy, S., Samyal, T., & Dahiya, S. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205614 · Cited by 12 · see citing works
DeepFake-o-meter v2.0: An Open Platform for DeepFake Detection
Ju, Y., Sun, C., Jia, S., Hou, S., Si, Z., Datta, S. K., Ke, L., Zhou, R., Nikolich, A., & Lyu, S. (2024) · IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) · doi:10.1109/mipr62202.2024.00075 · Cited by 13 · see citing works
Self-citationCan deepfakes manipulate us? Assessing the evidence via a critical scoping review
Ching, D., Twomey, J., Aylett, M. P., Quayle, M., Linehan, C., & Murphy, G. (2025) · PLoS ONE · doi:10.1371/journal.pone.0320124 · Cited by 14 · see citing works
Detection of Real-Time Deepfakes in Video Conferencing with Active Probing and Corneal Reflection
Hui, G., Wang, X., & Lyu, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094720 · Cited by 12 · see citing works
Self-citationAI vs. Human Vision: A Comparative Analysis for Distinguishing AI-Generated and Natural Images
Purohit, R., Sane, Y., Vaishampayan, D., Vedantam, S., & Singh, M. (2024) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect60202.2024.10469620 · Cited by 11 · see citing works
DeepExplain: Enhancing DeepFake Detection Through Transparent and Explainable AI model
Sunkari, V., & Srinagesh, A. (2024) · Informatica · doi:10.31449/inf.v48i8.5792 · Cited by 18 · see citing works
A Study on Deep Fake Face Detection Techniques
Arya, M., priyanshu, P., Upwan, Akash, A., Goyal, U., & Chawla, S. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575149 · Cited by 11 · see citing works
Understanding strategies employed by seniors in identifying deepfakes
Tang, Z., Goh, D. H., Lee, C. S., & Yang, Y. (2024) · Aslib Journal of Information Management · doi:10.1108/ajim-03-2024-0255 · Cited by 11 · see citing works
Syllaidopoulos, I., Ntalianis, K., & Salmon, I. (2025) · IEEE Access · doi:10.1109/access.2025.3572348 · Cited by 15 · see citing works
Shahzad, S. A., Hashmi, A., Peng, Y., Tsao, Y., & Wang, H. (2025) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2025.3618409 · Cited by 16 · see citing works
A Comparative Study on Deepfake Detection Algorithms
Kaushal, A., Singh, S., Negi, S., & Chhaukar, S. (2022) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n56670.2022.10074593 · Cited by 12 · see citing works
Spatio-Temporal Convolutional Neural Networks for Deepfake Detection: An Empirical Study
Sharma, V. K., Garg, R., & Caudron, Q. (2023) · International Conference on Informatics (ICI) · doi:10.1109/ici60088.2023.10420892 · Cited by 10 · see citing works
FAME: a lightweight spatio-temporal network for model attribution of face-swap deepfakes
Ahmad, W., Peng, Y., & Chang, Y.-H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128571 · Cited by 10 · see citing works
A Systematic Review on Deepfake Technology
Khan, I. R., Aisha, S., Kumar, D., & Mufti, T. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7615-5_55 · Cited by 11 · see citing works
A Note on Deepfake Detection with Low-Resources
Kawa, P., & Syga, P. (2020) · arXiv · doi:10.48550/arxiv.2006.05183 · Cited by 10 · see citing works
- Authors
- Lyu, S.
- Affiliations
- University at Albany, State University of New York (US)
- Corresponding author
- Lyu, Siwei
- ORCID
- Lyu, Siwei
- Venue
- 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
- Publisher
- IEEE
- Published
- 2020-07
- Pages
- 1-6
- OpenAlex
- W3012725859
- Cited by
- 234
- References
- 34
- Citations by year
- 2026: 18 · 2025: 40 · 2024: 54 · 2023: 54 · 2022: 40 · 2021: 23 · 2020: 5
- FWCI
- 9.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Multimedia & Expo Workshops (ICMEW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE RESOURCE CENTERS
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- deepfake detection, synthetic media, fake videos, AI-generated content, digital evidence, misinformation
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