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Deepfake Video Detection through Optical Flow Based CNN
Open accessAbstract
Recent advances in visual media technology have led to new tools for processing and, above all, generating multimedia contents. In particular, modern AI-based technologies have provided easy-to-use tools to create extremely realistic manipulated videos. Such synthetic videos, named Deep Fakes, may constitute a serious threat to attack the reputation of public subjects or to address the general opinion on a certain event. According to this, being able to individuate this kind of fake information becomes fundamental. In this work, a new forensic technique able to discern between fake and original video sequences is given; unlike other state-of-the-art methods which resorts at single video frames, we propose the adoption of optical flow fields to exploit possible inter-frame dissimilarities. Such a clue is then used as feature to be learned by CNN classifiers. Preliminary results obtained on FaceForensics++ dataset highlight very promising performances.
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“the re- cent common underlying mechanism for deepfake cre- ation is deep learning models such as autoencoders and generative adversarial networks (GANs)”
From Deep learning for deepfakes creation and detection: A survey (2022) · cites this paper as [7]
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“Amerini etal. (2019) have used CNN with optical flow to differentiate between fake and real videos.”
From Deepfake video detection: challenges and opportunities (2024) · cites this paper as Amerini et al. 2019
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Cites
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Optical Flow-Attention Fusion Model for Deepfake Detection
Jiang, Z., Zhao, P., & Zheng, Z. (2022) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3579731.3579810 · Cited by 2
“Inspired by [34] , We found that the changes of optical flow field between adjacent frames in lip, eye and other areas in fake video are different from those in real video.”
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Deep fake detection using deep learning techniques
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“A method using optical flow vectors derived for two successive frames was presented by Amerini et al. [9].”
MethodDeepfake Detection Model Based on VGGFace with Head Pose Estimation Technique
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“MesoNet is a lightweight convolutional neural network designed for facial recognition, proposed by Afchar et al. in 2018 [ 13 ] .”
Ain, Q. U., Javed, A., Malik, K. M., & Irtaza, A. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647949 · Cited by 2
Detecting Deepfake Videos Through CNN-MLP Model in Media Forensics
Kandari, M., Tripathi, V., Pant, B., Sar, A., Choudhury, T., & Choudhury, T. (2024) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon60325.2024.10687433 · Cited by 2
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BackgroundInfluentialEnhancing Deepfake Detection for Public Awareness, Law Enforcement and Legal Verification
Ng, J. Y., Chong, S. C., & Wee, K.-K. (2024) · International Conference on Information Technology Research and Innovation (ICITRI) · doi:10.1109/icitri62858.2024.10699122 · Cited by 2
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BackgroundAn 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
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Martin, N., Nambayil, S. R., Devikrishna, U., Joel Jismon, P., & Fasila, K. A. (2024) · IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) · doi:10.1109/spices62143.2024.10779815 · Cited by 3
MVFNet: Multipurpose Video Forensics Network using Multiple Forms of Forensic Evidence
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DeepFake Detection Using CNN Models with Explainable AI Techniques
Bipin, P. R., Biju, A. K., Balagopal, M., Shyam, B., & Adithya, C. (2025) · International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) · doi:10.1109/access65134.2025.11135663 · Cited by 3
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Investigating the Impact of Visual Attention Models in Face Forgery Detection
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“Amerini et al. [25] employed the use of optical.”
MethodSAFE: Sequential Attentive Face Embedding with Contrastive Learning for Deepfake Video Detection
Jung, J., Kang, C., Yoon, J., Woo, S. S., & Han, J. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615279 · Cited by 2
“So far, most of prior deepfake detection methods have primarily focused on image-based approaches [2, 13, 45], detecting deepfake in a given image or in a snapshot of a given video.”
BackgroundReddy, R. V., Nethi, A., Sukhija, S., & Gupta, Y. (2023) · International Conference on Emerging Techniques in Computational Intelligence (ICETCI) · doi:10.1109/icetci58599.2023.10331449 · Cited by 3
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“Amerini et al.(18) used light field information as a clue to identify true and fake videos.”
MethodA Novel Deepfake Detection Framework using Audio-Video-Textual FeaturesPreprint
Asha, S., Vinod, P., Amerini, I., & Menon, V. G. (2022) · Research Square · doi:10.21203/rs.3.rs-2390408/v1 · Cited by 2
Self-citationA 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
Local Corner and Motion Key Point Trajectory Extraction for Facial Forgery Identification
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Enhance Deepfake Video Detection Through Optical Flow Algorithms-Based CNN
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“There are also research works address deepfake detection from various perspectives, such as the concepts of representation learning and knowledge distillation [7]–[9], optical flow [10] and data augmentation algorithms [11], [12].”
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METODE DEEP LEARNING DALAM TEKNOLOGI DEEPFAKE : SYSTEMATIC LITERATURE REVIEW
Fernandes, Y. A., & Fatma, Y. (2025) · JATI (Jurnal Mahasiswa Teknik Informatika) · doi:10.36040/jati.v9i2.12987 · Cited by 1
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RetractedRaghavajosyula, V., & Pawar, D. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21320-8 · Cited by 4
“In another model [10 ], the optical flow field is suggested to leverage potential inter-frame dissimilarities.”
Deepfake detection using a distinctive eye signature and the entropy heat map of the image texture
Tchaptchet, E., Tagne, E. F., Djimeli-Tsajio, A., Acosta, J. C., Rawat, D. B., & Kamhoua, C. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21276-9 · Cited by 1
Behind the Pixels: Unveiling Deepfake Videos
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“[19][20] PRNU Artifact Detection: We used Photo Response Non-Uniformity (PRNU) analysis to identify artifacts in static frames, as explained in [2].”
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“Similarly, for image-based detection, the proposed method performs significantly better than previous high-performing methods such as I et al. [17] (91.21”
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Classification of Real and AI Generated Images Using Lightweight Deep Neural Networks
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“Techniques such as [6], [7], [9] rely on large model architectures, which are unsuitable for deployment on edge devices.”
Efficient deepfake detection: a hybrid lightweight framework for cross-domain generalization
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Unit-Selection Based Facial Video Manipulation Detection
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Manipulated Face Detection and Localization Based on Semantic Segmentation
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Hybrid Spatio-Temporal Network for Face Forgery Detection
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Uncertainty-Guided Test-Time Training for Face Forgery Detection
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“In the earlier stage, methods[2,18,1,7,28,13] are built with a significant emphasis on spotting semantic visual artefacts with sophisticated model designs.”
MethodIntegration Model of Deep Forgery Video Detection Based on rPPG and Spatiotemporal Signal
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AE-LSTM: A Hybrid Approach for Detecting Deepfake Videos in Digital Forensics
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A survey on deepfake detection through deep learning
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“[5] have introduced a system leveraging optical flow techniques to detect inter-frame dissimilarities, which are then utilized as features for CNN classifiers to learn.”
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Deepfake Detection Using Neural Networks
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Spatiotemporal Deepfake Video Detection: A Hybrid CNN-Transformer Approach with Frequency Analysis
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“Many techniques take advantage of the strength of deep networks directly, by training image classifiers with various architectures to identify forgeries, either through RGB [4, 2, 23, 7] or optical flow [3].”
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Audio-Video Analysis Method of Public Speaking Videos to Detect Deepfake Threat
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“Amerini et al. [20] explored the differences in optical flow between frames, and used VGG-16 to learn and classify the differences in optical flow, because optical flow is calculated by the motion differences between successive frames, and the motion differences between naturally shot and forged…”
MethodDeepFake Image Detection Using Adaptive Discriminator Augmentation (ADA)
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“Amerini et al. [29] suggested a detection approach that took use of the differences in optical flow between fake and real faces.”
BackgroundKumar, S., Kapoor, N., & Sharma, K. (2024) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt61202.2024.10489611
“Deepfake Video Detection through Optical Flow based CNN[11] proposed by Irene Amerini, et.al uses optical flow fields to distinguish a forged from an original video.”
BackgroundMethodVideo Morphing Attack Detection Using Convolutional Neural Networks On Deep Fake Detection Algorithm
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Large-scale datasets for facial tampering detection with inpainting techniques
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Perspective on distinguishability between human and Artificial Intelligence
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DEEPFAKE DILEMMA: A REVIEW STUDY ON VIDEO SYNTHESIS TECHNOLOGY
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A comprehensive survey of detecting deepfakes techniques
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Assessing Backdoor Risk in Deepfake Detection
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Using Microposture Features and Optical Flows for Deepfake Detection
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Enhancing Deepfake Detection via Adversarial Generative Learning
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Deepfake Multi-Faces Detection Using Haar Cascades with Sift Matching AlgorithmPreprint
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PerceptiGuard: Unveiling DeepFakes Through Enhanced Media Perception
Phatangare, S., Tekade, O., Thakare, P. P., Wankhade, A., & Wankhade, P. (2024) · IEEE Silchar Subsection Conference (SILCON) · doi:10.1109/silcon63976.2024.10910356
“[ 14] presents a unique technique that examines convolutional traces left by generative models to identify DeepFake photos.”
A Study on Existing Deep Learning Models for Deepfake Detection
Rawat, A., Yadav, S., Choudhary, V., Jain, I., & Seeja, K. R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8526-1_17
Deepfake Detection Systems: A Comprehensive Survey of Algorithms and TechniquesPreprint
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Studi Bibliometrik Tren Teknologi Deepfake dan Tantangan Etisnya di Dunia Digital
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Deepfake Video Detection: A CSP-Dencenet and LSTM Based Approach
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A Comprehensive Review of Deepfake Detection Technologies
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DEVision: A Lightweight Model for Deepfake Video DetectionPreprint
Ghosh, P. S., Paul, R., Koley, S., Kumar, P., Sarkar, D., Chowdhury, R., & Goto, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5472931
Enhancing Face Forgery Detection with Augmented Feature Distillation
He, Y. L., Zhou, W., Zhao, H., Fang, Z., Li, X., Huangfu, M., Zhang, W., & Yu, N. (2025) · IEEE International Conference on Privacy Computing and Data Security (PCDS) · doi:10.1109/pcds65695.2025.00025
“To capture more generalized facial forgery representations, some studies have focused on hand-crafted features, such as eye blinking patterns[8], optical flow inconsistencies[9], erasing techniques[10], and mouth movements[11].”
Singh, R., & Agarwal, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-032-03751-0_34
Adversarial Reality for Frame-based Deepfake Detectors
Çiftçi, U. A., Solar, N., Greene, E. M., Rhodes, A. D., & Demir, İ. (2025) · doi:10.1145/3746265.3759662 · Cited by 2
“Mainly, detectors operate on raw data [1, 3, 8, 30, 44, 44, 54, 58, 62, 67, 69, 72, 81, 93, 94], or signs of authenticity or fak-ery [6, 9, 13, 21, 22, 29, 38, 47, 49, 51–53, 59, 59, 60, 73, 76, 79, 92].”
Proactive Deepfake Detection via Self-Verifiable Semantic Watermarking
Jiang, P., Lei, B., Sun, Y., Yu, L., Chen, Z., Xie, H., & Zhang, Y. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755587
“Our main contributions are summarized as follows: • We propose a self-verifiable watermarking strategy via facial semantics including identity and attributes, which brings a new paradigm for facial digital watermarking.”
Esperanza, J., Marquez, J. F., & Sy, R. A. (2025) · Isabela State University Linker Journal of Engineering Computing and Technology · doi:10.65141/ject.v2i1.n8
Quantitative Analysis of Deepfake Detection Using XceptionNet and MesoNet
Ciang, A., Hapsari, C. H., Sun, S., Pranoto, H., & Hermawan, E. S. (2025) · International Conference on Artificial Intelligence and Data Sciences (AiDAS) · doi:10.1109/aidas67696.2025.11213834
“A systematic review by Amerini et al. [22] and a survey by Saikia et al. [23] highlight that domain overfitting remains a critical issue.”
Enhancing Visual Speaker Authentication using Dynamic Lip Movement and Meta-Learning
Pathare, P., & Bajwa, G. (2025) · International Conference on Privacy, Security and Trust (PST) · doi:10.1109/pst65910.2025.11268841
“Similarly, Amerini et al. [10] demonstrated that optical flow features can reveal DeepFake inconsistencies, yet their method was not tailored for user-specific adaptation, resulting in limited effectiveness when faced with entirely new identities.”
InfluentialSTFANet: A spatial and temporal feature aggregation network for fake face detection in videos
YAO, G. L. J., Yang, G., Liu, X., & Chen, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0337340
“For instance, Amerini et al. [14] investigated motion anomalies between a person and the background by leveraging optical flow.”
Deep-Fakes Detection in Media Forensics Using Deep Learning and Fusion Methods
Deshmukh, Y., Haque, M. J., & Bhaladhare, P. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362482
Deepfakes Generation and Identification Methods Using Deep Learning Techniques
All, D. J., & Obaid, A. J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7526-5_32
Hybrid Deep Learning Approach For Deepfake Video Detection Using Feature Fusion
Srithan, D., Sravan, G., Venkatesh, J., & Jabbar, M. A. (2025) · International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) · doi:10.1109/icerect65215.2025.11377400
“[16].”
Anitha, G., Saran, A., V, Loganathan, N., et al. (2025) · International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) · doi:10.1109/icerect65215.2025.11378357
“In a similar direction, MesoNet, proposed by Afchar et al., offered a lightweight CNN-based approach that achieved approximately 84.3% accuracy, but performed poorly on low-quality and compressed videos due to its shallow architecture [6].”
InfluentialSpot the Fake: Vision Transformers in the Fight Against DeepFakes
Pandya, K., & DAHIYA, V. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-17837-4_24
Deepfake Detection: Bi-LSTM Framework with MobileNetV2
Premanand, V., Akil, S., & Aniruth Narayanan, S. (2025) · International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE) · doi:10.1109/icetce66848.2025.11388101
“Through their [2] survey Peipeng Yu et al. examined deep-fake detection systems completely by assessing their positive aspects and weaknesses.”
Is It Certainly a Deepfake? Reliability Analysis in Detection & Generation Ecosystem
Köse, N., Rhodes, A. D., Çiftçi, U. A., & Demir, İ. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00035 · Cited by 2
“Deepfake detectors first focused on artifacts of fakery , learning directly from data with “blind” detectors [8, 10, 13, 29, 30, 36, 45, 51, 64, 78, 79, 79].”
Does data augmentation help or hinder the generalization of deepfake video detection?
Kaddar, B., Fezza, S. A., Boutellaa, E., Hamidouche, W., & Hadid, A. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21458-5 · Cited by 1
“Complementary meth-ods exploit specific traces, including warping artifacts [ 25] and temporal inconsistencies via optical flow [ 26].”
Deepfake Detection System Using Hybrid Deep Learning for Images and Videos
Sunil Kumar, K. N., Reddy, G. V. J. P., Guru Prakash, B., & Naveen, K. (2026) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect68478.2026.11426158
A Study on Deep Fake Detection and Content Suspension Mechanisms from Creation to Control
Rallapalli, S., Sowmya, J., & Dileep, M. R. (2025) · International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT) · doi:10.1109/icrteect67512.2025.11448651
Chen, H., Zhang, Z., Li, Q., & Feng, C. (2026) · Algorithms · doi:10.3390/a19040270
Towards lighter and faster deep neural networks with parameter pruning
Hubens, N. (2022) · theses.fr (ABES) · doi:10.70675/c018d7baz3aa3z43b2z9f61z0d9bb05ff94e
Robustesse aux attaques en authentification digitale par apprentissage profond
Trabelsi, A. (2022) · theses.fr (ABES) · doi:10.70675/1fb5b427z188fz4a55z88a7z4bb32bbcaf4a
Metadata
- Authors
- Amerini, I., Galteri, L., Caldelli, R., & Del Bimbo, A.
- Affiliations
- University of Florence (IT)
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (IT)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
- Publisher
- IEEE
- Published
- 2019-10
- Volume
- 2019
- Pages
- 1205-1207
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2995516027
- Cited by
- 424
- References
- 14
- Citations by year
- 2026: 35 · 2025: 70 · 2024: 77 · 2023: 95 · 2022: 71 · 2021: 49 · 2020: 27
- FWCI
- 11.98
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE International Conference on Computer Vision Workshops (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, other-oa)
- IEEE Conference Proceedings
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- deepfake detection, optical flow, convolutional neural networks, manipulated video, video forensics, FaceForensics++, fake information
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