The DOI matches its Crossref record; 30 of the 34 references it cites were found in a registry or an index.
Deepfake Detection: Current Challenges and Next Steps
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The 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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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 20,507
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
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
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
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
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×
“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
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
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
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
Research works printed but not linked: 1
- goodfellow, 2014, Generative adversarial nets, Advances in Neural Information Processing Systems (NIPS)
Cited by
Sorted by citations
Items 101–200 of 235
The Rise of Deepfake Technology
Akbar, M., Suaib, M., & Hussain, M. S. (2023) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-6684-8133-2.ch010 · Cited by 11
Deepfakes in Cyber Warfare: Threats, Detection, Techniques and Countermeasures
Cinar, B. (2023) · Asian Journal of Research in Computer Science · doi:10.9734/ajrcos/2023/v16i4381 · Cited by 10
How Are Deepfake Videos Detected? An Initial User Study
Thaw, N. N., July, T., Wai, A. N., Goh, D. H., & Chua, A. Y. K. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-78635-9_80 · Cited by 10
Combating disinformation with AI: Epistemic and ethical challenges
Lange, B., & Lechterman, T. M. (2021) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas52410.2021.9629122 · Cited by 9
Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images
Santosh, Lin, L., Amerini, I., Wang, X., & Hu, S. (2024) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss61716.2024.10672612 · Cited by 17
Fraile-Narváez, M., Sagredo-Olivenza, I., & McGowan, N. (2022) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2022.11.005 · Cited by 9
FakePoI: A Large-Scale Fake Person of Interest Video Detection Benchmark and a Strong Baseline
Tian, L., Yao, H., & Li, M. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3269742 · Cited by 9
Ruffin, M., Seo, H., Xiong, A.-P., & Wang, G. (2024) · International Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31392 · Cited by 9
Chakraborty, R., & Naskar, R. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100677 · Cited by 15
Manisha, M., Li, C., & Kotegar, K. A. (2025) · IEEE Access · doi:10.1109/access.2025.3537558 · Cited by 8
The harm of deepfakes: a scoping review of deepfakes’ negative effects on human mind and behavior
Diel, A., Lalgi, T. J., Mellis, F. S., Teufel, A., & Bäuerle, A. (2025) · AI & Society · doi:10.1007/s00146-025-02774-0 · Cited by 12
Deepfakes and the promise of algorithmic detectability
Jacobsen, B. N. (2024) · European Journal of Cultural Studies · doi:10.1177/13675494241240028 · Cited by 9
Bale, D., Ochei, L. C., & Ugwu, C. (2024) · International Journal of Intelligent Information Systems · doi:10.11648/j.ijiis.20241302.11 · Cited by 7
Deepfake detection for preventing Audio and Video frauds using Advanced Deep Learning Techniques
Bharadwaj, R., Ratnaparkhi, S., Rajpurohit, R., Rahate, K., Pandita, R., & Thosar, S. (2023) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics59993.2023.10421486 · Cited by 7
Identifying Invariant Texture Violation for Robust Deepfake DetectionPreprint
Sun, X., Wu, B., & Chen, W. R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10580 · Cited by 9
Automatic building of fault&tolerant on&board networks
Olenev, V. L. (2023) · H&ES Research · doi:10.36724/2409-5419-2023-15-4-4-13 · Cited by 6
Exploring Deepfake Detection: Techniques, Datasets and Challenges
Rana, P., & Bansal, S. R. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/160156 · Cited by 9
Deep Fake Detection: Unmasking the Illusion using CNN and LSTM
Niranjani, V., S, A. S., Devamitra, T., & Jagapreetha, B. (2023) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia60377.2023.10426523 · Cited by 6
Dhar, A., & Agrawal, E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-0641-9_37 · Cited by 6
Sun, Y., Zhang, Z., Qiu, C., Wang, L., Sun, L., & Wang, Z. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778420 · Cited by 10
Hu, B., & Huang, G. (2025) · Social Media + Society · doi:10.1177/20563051251401815 · Cited by 7
Ahmad, W., Peng, Y., Chang, Y.-H., Ganfure, G. O., & Khan, S. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3715138 · Cited by 8
Saber, A. M., Hassan, M. T., Mohamed, M. S., ELHusseiny, R., Eltaher, Y. M., Abdelrazek, M., & Omar, Y. K. (2022) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc55081.2022.9781791 · Cited by 5
Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images
Hadwiger, B., & Rieß, C. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3189527 · Cited by 6
Deepfake Creation and Detection using Ensemble Deep Learning Models
Rao, S., Shelke, N. A., Goel, A., & Bansal, H. (2022) · International Conference on Contemporary Computing · doi:10.1145/3549206.3549263 · Cited by 5
Audio-Visual Deepfake Detection System Using Multimodal Deep Learning
Parikh, A., Pereira, K., Kumar, P. S., & Devadkar, K. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205804 · Cited by 5
Understanding Major Topics and Attitudes Toward Deepfakes: An Analysis of News Articles
Tang, Z., Yin, S. X., & Goh, D. H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48044-7_25 · Cited by 5
Detection of Deepfake Images and Videos Using SVM, CNN, and Hybrid Approaches
Stankov, I. S., & Dulgerov, E. E. (2024) · International Scientific Conference Electronics (ET) · doi:10.1109/et63133.2024.10721497 · Cited by 5
DeepFake Creation and Detection Using LSTM, ResNext
Patel, D. M., Motiani, J., Patel, A. M., & Bohara, M. H. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-7610-9_75 · Cited by 5
Impact of Deepfake Technology on Digital World Authenticity: A Review
Ahmed, S. (2022) · International Journal of Engineering and Management Research · doi:10.31033/ijemr.12.3.10 · Cited by 6
Generalized Deepfake Detection Algorithm Based on Inconsistency Between Inner and Outer Faces
Gao, J., Concas, S., Orrù, G., Feng, X., Marcialis, G. L., & Roli, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-51023-6_29 · Cited by 5
Robust deepfake detection using multi-scale feature fusion
Yogarajan, G., Soundhariya, S., & Harini, R. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20768-4 · Cited by 4
Swenson, A. (2023) · Brazilian Creative Industries Journal · doi:10.25112/bcij.v3i2.3538 · Cited by 4
Survey on Vision based Fake News Detection and its Impact Analysis
Raval, M. S., Roy, M., & Kuribayashi, M. (2022) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.23919/apsipaasc55919.2022.9980089 · Cited by 5
Towards mitigating uncann(eye)ness in face swaps via gaze-centric loss terms
Wilson, E., Shic, F., Jörg, S., & Jain, E. (2024) · Computers & Graphics · doi:10.1016/j.cag.2024.103888 · Cited by 7
Artifact Based Deepfake Detection Methods
Preeti, P., & Bansal, S. R. (2023) · International Conference on Informatics (ICI) · doi:10.1109/ici60088.2023.10421629 · Cited by 5
Gan-Enhanced Real-Time Detection of Deepfakes Videos
Fatima, A., & Ram, P. K. (2024) · Journal of Artificial Intelligence and Capsule Networks · doi:10.36548/jaicn.2024.4.005 · Cited by 4
Image-based DeepFake Detection Using Artificial Intelligence
Singh, S. K., Kumar, G., Dubey, A. K., Yadav, R., Rai, V., & Sharma, A. K. (2025) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt64827.2025.10968600 · Cited by 4
Attention Guided Spatio-Temporal Artifacts Extraction for Deepfake Detection
Wang, Z., Li, X. S., Ni, R., & Zhao, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-88013-2_31 · Cited by 4
Fakequipo: Deep Fake Detection
Bide, P. J., Varun, V., Patil, G., Shah, S., & Patil, S. (2022) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat55367.2022.9972149 · Cited by 4
Understanding Users’ Deepfake Video Verification Strategies
Goh, D. H., Lee, C. S., Chen, Z., Kuah, X. W., & Pang, Y. L. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-19682-9_4 · Cited by 4
Autonomous detection and evaluation of deepfakes: a comprehensive study
Sunil, R., Mer, P., & Diwan, A. (2023) · 2023 Seventh International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip61524.2023.10537789 · Cited by 4
Comparing Human and Machine Deepfake Detection with Affective and Holistic Processing.Preprint
Groh, M., Epstein, Z., Firestone, C., & Picard, R. W. (2021) · arXiv (Cornell University) · Cited by 4
Evaluating Features and Variations in Deepfake Videos Using the CoAtNet Model
Alattas, E., Clark, J., Al-Aama, A. Y., & Jarraya, S. K. (2025) · Journal of Imaging · doi:10.3390/jimaging11060194 · Cited by 3
Convolutional Neural Network Based Detection Mechanism for Deepfake Image
Mishra, A., Rajhans, S., Gupta, B. B., & Chui, K. T. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10930100 · Cited by 4
Akbar, M., Suaib, M., & Hussain, M. S. (2022) · International Journal of Computer and Information Technology(2279-0764) · doi:10.24203/ijcit.v11i2.282 · Cited by 6
Kleinlogel, E. P., Renier, L. A., Mast, M. S., Jayagopi, D. B., & Shubham, K. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1069352 · Cited by 3
Combining Computer Vision Techniques and Intraframe Noise Methods to Detect a Deepfake
Shelke, M. P., Ranjan, N. M., Kharade, A., Gaikwad, P., Arakh, S., & Kalore, A. (2023) · Soft Computing Research Society eBooks · doi:10.56155/978-81-955020-2-8-43 · Cited by 3
Using Vocoder Artifacts For Audio Deepfakes Detection
Sun, C., AlBadawy, E. A., Davison, T. F., Robinson, S. R., Chang, M., & Lyu, S. (2023) · Advances in information security · doi:10.1007/978-3-031-49803-9_11 · Cited by 3
Self-citationDigital Forensics Triage Classification Model using Hybrid Learning Approaches
Mohmed Afridhi, L., & Palanivel, K. (2022) · International Journal of Innovative Research in Computer Science & Technology · doi:10.55524/ijircst.2022.10.3.7 · Cited by 4
Deepfake Face Identification Using Deep Learning Ensemble Methods
All, D. J., & Obaid, A. J. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7137-3_43 · Cited by 2
Secure Face and Liveness Detection with Criminal Identification for Security Systems
Shinde, P. S., & Raundale, A. R. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i8s.7231 · Cited by 2
DEZENFORMASYON’UN DÖNÜŞÜMÜ: SAHTE İÇERİKLER
Berk, M. E., & HASDEMİR, A. D. (2023) · Uluslararası Kültürel ve Sosyal Araştırmalar Dergisi (UKSAD) · doi:10.46442/intjcss.1401714 · Cited by 2
Peng, T., Zhou, Y., Sun, R., Luo, Y., & Li, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53311-2_7 · Cited by 2
Detection of DeepFake Videos Using Computer Vision and Deep Learning
Rahman, A., Rahman, F., Labib, T., Uschash, E. I., Adiba, S. J. C., & Karim, D. Z. (2023) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde59766.2023.10487745 · Cited by 2
Multi-level Distributional Discrepancy Enhancement for Cross Domain Face Forgery Detection
Qiu, L., Jiang, K., Liu, S., & Tan, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8499-8_35 · Cited by 2
An Efficient Framework to Recognize Deepfake Faces using a Light-weight CNN
Arshad, T., Khan, M. H., & Farid, M. S. (2024) · International Conference on Multimedia Systems and Signal Processing (ICMSSP) · doi:10.1145/3690063.3690064 · Cited by 4
Addressing Diffusion Model Based Counter-Forensic Image Manipulation for Synthetic Image Detection
Herur, A. N., Santhosh, V., Shetty, N., & Seelamantula, C. S. (2024) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3702250.3702296 · Cited by 3
Enhanced Detection of Deep Fakes: Exploring Advanced Approaches
Vayadande, K. B., Sawant, A., Pawar, A., Rajurkar, S., Shendre, R., & Waghmode, J. (2024) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscna63714.2024.10864123 · Cited by 2
Detection of Quality Deep Fake Images and Videos Using Customised Convolutional Neural Networks
Mahar, A., Verma, P., & Singh, A. (2025) · International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) · doi:10.1109/ce2ct64011.2025.10939891 · Cited by 2
Mahar, A., Verma, P., & Singh, A. (2025) · International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT) · doi:10.1109/ce2ct64011.2025.10939586 · Cited by 2
Jadhav, A., Narale, D., Kore, R., Shisode, U., & Kulange, A. (2024) · Asian Conference on Intelligent Technologies (ACOIT) · doi:10.1109/acoit62457.2024.10939366 · Cited by 2
CLH: Approach for Detecting Deep Fake Videos
Hedge, A. S., Vinutha, M., Supriya, K., Nagasundari, S., & Honnavalli, P. B. (2021) · Communications in computer and information science · doi:10.1007/978-981-16-8059-5_33 · Cited by 2
Das, A. K., Mukhopadhyay, S., Dalui, A., Bhattacharya, R., & Naskar, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49099-6_13 · Cited by 2
Lim, G., & Donovan, J. (2021) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197510636.013.44 · Cited by 1
A Novel Approach to Detect Low Quality Deepfake Videos
Guhagarkar, N., Desai, S., Vaishampayan, S., & Save, A. M. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-5157-1_68 · Cited by 1
OPPORTUNITIES, THREATS AND SOLUTION TECHNIQUES OF DEEP-FAKE TECHNOLOGY: A SYSTEMATIC REVIEW
Shamsabadi, A., Mojdeganlou, H., Barzegary, A., Fakhfouri, A., Azad, K., Heydari, M., & Mehraeen, Z. P. A. E. (2022) · IADIS International Conference on e-Society (ES) · doi:10.33965/es_ml2022_202202l003 · Cited by 1
Adhicitta, A. (2023) · bit-Tech · doi:10.32877/bt.v6i2.1000 · Cited by 1
Near Real-Time Deepfake Audio Detection based on Speaker Diarization Techniques
Bajwa, M. K. Z., Negru, C., Mocanu, B.-C., Castiglione, A., & Pero, C. (2025) · International Conference on Control Systems and Computer Science · doi:10.1109/cscs66924.2025.00067 · Cited by 1
RoGA: Towards Generalizable Deepfake Detection through Robust Gradient Alignment
Qiu, L., Jiang, K., & Tan, X. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209067 · Cited by 2
ZİHİNLERE ATILAN MERMİ: SİHİRLİ MERMİ KURAMI PERSPEKTİFİNDEN DEZENFORMASYON
AYDIN, A. F. (2025) · İNİF E - Dergi · doi:10.47107/inifedergi.1737898 · Cited by 1
Rana, P., Bansal, S., Nagar, S., & Malik, A. (2025) · International Conference on Sustainability, Innovation & Technology (ICSIT) · doi:10.1109/icsit65336.2025.11294796 · Cited by 1
Deepfake audio detection and justification with explainable artificial intelligence
Govindu, A., Kale, P., Hullur, A., Gurav, A., & Godse, P. (2026) · International Journal of Data Science and Analytics · doi:10.1007/s41060-026-01186-7 · Cited by 15
Recent Trends in Deepfake Detection
Sudarshana, K., & Mylarareddy, C. (2021) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-7728-8.ch001 · Cited by 2
Mikhaylov, V. Y., Богданович, М. Р., & Витальевна, В. О. (2023) · H&ES Research · doi:10.36724/2409-5419-2023-15-4-68-74 · Cited by 1
AE-LSTM: A Hybrid Approach for Detecting Deepfake Videos in Digital Forensics
Kandari, M., Tripathi, V., & Pant, B. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9811-1_38 · Cited by 1
Calderón-González, D., Ábalos, N., Bayo, B., Cánovas, P., Griol, D., Muñoz-Romero, C., Pérez, C., Vila, P., & Callejas, Z. (2024) · Applied Sciences · doi:10.3390/app14219916 · Cited by 1
Spatiotemporal Deepfake Video Detection: A Hybrid CNN-Transformer Approach with Frequency Analysis
Celebi, N., & Liu, Q. (2025) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri66576.2025.00048 · Cited by 1
Through the Digital Maze: Media Regulation for Ethical Journalism in a Disinformation AgePreprint
Sharma, R., & Rout, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4799566 · Cited by 1
Predictive Techniques for Identifying and Classifying Real and Ai-Generated VoicesPreprint
Shukla, M., Saraswat, A., Mallick, S., & Mishra, M. S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5035352 · Cited by 1
Aggarwal, A., Wadhwa, S., Gupta, P., Anand, N., & Kushwah, R. (2021) · doi:10.1109/icsc53193.2021.9673225
How Convincing are Fake Photos for Internet Users? A Study and Proof of ConceptPreprint
Hryniów, K., & Gutowski, B. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4073134
Довгаль, В. А. (2022) · Вестник Адыгейского государственного университета серия «Естественно-математические и технические науки» · doi:10.53598/2410-3225-2021-4-291-82-94
Deepfake Literacy of the Journalists and Initiatives of Media Organization in Bangladesh
Ali, M. S. (2022) · New Media and Mass Communication · doi:10.7176/nmmc/102-01
Kejriwal, M. (2022) · Future of business and finance · doi:10.1007/978-3-031-19039-1_6
F2DLNet: A Face Forgery Detection and Localization Network Based on SSIM Error Maps
Peng, F., Zhang, X., & Long, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-20099-1_30
SURVEY ON GUARDING AUTHENTICITY: “DEEPVISION’S EYE BLINK PATTERN ANALYSIS FOR DEEPFAKE DETECTION”
(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets46024
Školkay, A. (2023) · Środkowoeuropejskie Studia Polityczne · doi:10.14746/ssp.2023.2.13
Security analysis of NOMA systems
Alekseev, S. S., Kosichkina, T. P., Pankratov, D. Y., & Shamsutdinov, I. A. (2023) · H&ES Research · doi:10.36724/2409-5419-2023-15-4-38-46
Software tool for detecting fake video content using the Deepfake technology of the GAN algorithm
Dzhurov, A. A., Черкесова, Л. В., & Revyakina, E. A. (2023) · H&ES Research · doi:10.36724/2409-5419-2023-15-4-60-67
Evaluation of the electromagnetic accessibility of radio emission sources Starlink GSS
Sevidov, V., Dvornikov, S. S., Bestugin, A. R., Dvornikov, S. V., & Киршина, И. А. (2023) · H&ES Research · doi:10.36724/2409-5419-2023-15-4-26-37
Overview of key features in the construction of underwater optical wireless communication
Pavlov, I. I., Pavlova, M. S., Abramova, E. S., Abramov, S. S., & Shcherbakov, Y. S. (2023) · H&ES Research · doi:10.36724/2409-5419-2023-15-4-14-25
Third platform for informatization and big data
Moshak, N. N., Романовна, Р. С., & Gruzdev, A. A. (2023) · H&ES Research · doi:10.36724/2409-5419-2023-15-4-47-59
ENHANCING DEEPFAKE DETECTION: LEVERAGING MTCNN AND INCEPTION RESNET
Barpha, V. S. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem33601
A Survey an Optimized Dense CNN Model for Recognizing Deepfake Images
Gachchannavar, M., -, N. J. R., & -, R. V. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i04.26429
WHO: Human Perspectives on Humane Technology
Walther, C. C. (2024) · Springer eBooks · doi:10.1007/978-3-031-67823-3_3
Unmasking Deepfake Images and Videos: A Comprehensive Detection
Parkhi, P. N., Patel, A., Solanki, D., Ganwani, H., & Anandani, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70789-6_30
Hierarchical ST-CEN with Dynamic Attention Mechanisms for Enhanced Deepfake Image Detection
Prabith, G. S., & Anjali, T. (2023) · International Conference on Intelligent Computing, Communication & Convergence (ICI3C) · doi:10.1109/ici3c60830.2023.00078
Improving Deepfake Detection Using Deep Learning Techniques
Nair, H., Tiwari, A., Rahat, N., Kaliyar, R. K., & Yadav, A. (2024) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces63552.2024.10859432
ForgeryDetect: AI-driven Deep Fake Identification
Bharti, A., Sinha, V. K., Peter, J. S. P., & Priya Esther, B. (2024) · International Conference on Recent Innovation in Smart and Sustainable Technology (ICRISST) · doi:10.1109/icrisst59181.2024.10922001
Metadata
- 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
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
