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Detecting Deep-Fake Videos from Appearance and Behavior
Open accessAbstract
Synthetically-generated audios and videos - so-called deep fakes - continue to capture the imagination of the computer-graphics and computer-vision communities. At the same time, the democratization of access to technology that can create a sophisticated manipulated video of anybody saying anything continues to be of concern because of its power to disrupt democratic elections, commit small to large-scale fraud, fuel disinformation campaigns, and create non-consensual pornography. We describe a biometric-based forensic technique for detecting face-swap deep fakes. This technique combines a static biometric based on facial recognition with a temporal, behavioral biometric based on facial expressions and head movements, where the behavioral embedding is learned using a CNN with a metric-learning objective function. We show the efficacy of this approach across several large-scale video datasets, as well as in-the-wild deep fakes.
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“Existing unimodal deepfake detectors are primarily designed to detect a single type of manipulation, such as visual, acoustic, and text.”
From Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization (2024) · cites this paper as [2]
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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 [2]
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Cites
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Cited by
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Items 101–200 of 245
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
“…era [63], [64], [65], [66], [67], [68], [69], [70], there has been an increasing development in deepfake detection [17], [27], [58], [59], [71], [72], [73], [74], [75], [76], [77], [78], [79] because individuals are aware that the abuse of deepfake technology may have a negative impact on society.”
BackgroundDeepfakes Catcher: A Novel Fused Truncated DenseNet Model for Deepfakes Detection
Khalid, F., Javed, A., Irtaza, A., & Malik, K. M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9331-2_20 · Cited by 9
Novel 59-layer dense inception network for robust deepfake identification
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Hierarchical Learning and Dummy Triplet Loss for Efficient Deepfake Detection
Beuve, N., Hamidouche, W., & Olivier Déforges, A. (2023) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3626101 · Cited by 8
Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext
Abidin, M. I., Nurtanio, I., & Achmad, A. (2022) · ILKOM Jurnal Ilmiah · doi:10.33096/ilkom.v14i3.1254.178-185 · Cited by 8
An unsupervised approach to detect review spam using duplicates of images, videos and Chinese texts
Li, J., Zhang, P., & Yang, L. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101186 · Cited by 8
Effects of Browsing Conditions and Visual Alert Design on Human Susceptibility to Deepfakes
Josephs, E., Fosco, C. L., & Oliva, A. (2024) · Journal of Online Trust and Safety · doi:10.54501/jots.v2i2.144 · Cited by 9
Channel-Wise Spatiotemporal Aggregation Technology for Face Video Forensics
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A Lightweight CNN for Efficient Deepfake Detection of Low-resolution Images in Frequency Domain
Sabareshwar, D., Raghul, S., Ravi, S., Varalakshmi, M., & U, P. M. P. (2024) · International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) · doi:10.1109/ic-etite58242.2024.10493406 · Cited by 7
“As suggested in [19], a biometric-based forensic technique can be efficiently used for detecting deep face-swap impersonations.”
BackgroundThe Threat of Deepfakes to Computer and Human Visions
Korshunov, P., & Marcel, S. (2022) · Advances in computer vision and pattern recognition · doi:10.1007/978-3-030-87664-7_5 · Cited by 7
The Eyes: A Source of Information for Detecting Deepfakes
Tchaptchet, E., Tagne, E. F., Acosta, J. C., Kamhoua, C., & Rawat, D. B. (2025) · Information · doi:10.3390/info16050371 · Cited by 7
Exploring the Advancements and Challenges of Deepfake Face-swap: A Survey
Bakar, S. A. R. B. S. A., Waseem, S., Omar, Z., & Bilalashfaqahmed (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21285-8 · Cited by 13
A Bibliometric Analysis of Deepfakes : Trends, Applications and Challenges
Garg, D., & Gill, R. (2024) · ICST Transactions on Scalable Information Systems · doi:10.4108/eetsis.4883 · Cited by 7
Improvised detection of deepfakes from visual inputs using light weight deep ensemble model
Panda, S. K., Diwan, T., Kakde, O. G., & Tembhurne, J. V. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14307-8 · Cited by 6
“In [1], a biometric-based forensic technique is designed for detecting face-swap Deepfakes.”
MethodFace Forgery Detection Algorithm Based on Improved MobileViT Network
Wang, T., & Lu, X. (2023) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp58490.2023.10248802 · Cited by 6
Katamneni, V. S., Nadimpalli, A. V., & Rattani, A. (2024) · Springer eBooks · doi:10.1007/978-981-97-2059-0_8 · Cited by 6
LatentForensics: Towards Frugal Deepfake Detection in the StyleGAN Latent Space
Delmas, M., & Séguier, R. (2024) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta63115.2024.00020 · Cited by 6
“MesoNet [2], for instance, is a lightweight convolutional network which relies on the density of details in image patches to detect whether or not an image have been tampered with.”
Image Feature Detectors for Deepfake Image Detection Using Transfer Learning
Alheeti, K. M. A., Al-Rawi, S. S., Khalaf, H. A., & Dosary, D. A. (2021) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese54285.2021.9719332 · Cited by 6
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“To conclude, recently in [1] the authors describe a biometric-based forensic technique for detecting face-swap deepfakes tested against several large-scale video datasets, as well as in-the-wild deepfakes.”
BackgroundIDGuard: Robust, General, Identity-Centric POI Proactive Defense Against Face Editing Abuse
Dai, Y., Fei, J., & Huang, F. (2024) · 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/cvpr52733.2024.01134 · Cited by 5
Ain, Q. U., Javed, A., Malik, K. M., & Irtaza, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20268-x · Cited by 7
“For the WLDR dataset, the proposed model was compared with the contemporary methods in [31, 43, 57, 58, 60].”
InfluentialGeneralized Deepfake Detection Using Identity, Behavioral, and Geometric Signatures
Farooq, M. U., Khan, A. A., Haq, I. U., & Malik, K. M. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3588348 · Cited by 7
“…framework with PWL [18] and AnB [10] on WLDR dataset while 2-stream [60], XceptionNet [53], Head Pose [16], MesoNet [61], DSP-FWA [11], AnB [10], MAT [62], SLADD [63], Face-x-ray [64], PCL + 12G [59], and SBI [65] on the rest of the datasets. the CelebDF dataset, outperforming other…”
InfluentialResearch on video face forgery detection model based on multiple feature fusion network
Hou, W., Sun, J., Liu, H., & Zhang, F. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03059-7 · Cited by 5
“Agarwal et al. [12] combines static biometrics traits with temporal behavioral biometrics traits based on facial expressions and head movements.”
AI-based Identity Fraud Detection: A Systematic Review
ZHANG, C., Gill, A. Q., Liu, B., & Anwar, M. J. (2026) · Applied Artificial Intelligence · doi:10.1080/08839514.2026.2731995 · Cited by 19
“…networks specifically designed to extract facial features, such as Viola-Jones Object Detection and Multi-task Cascaded Convolutional Networks [23], [37], are also applied during the training process, particularly targeting complex attacks like face-swap, face replacement, and face…”
InfluentialIs this real? Susceptibility to deepfakes in machines and humans
Pehlivanoglu, D., Zhu, M., Zhen, J., Gagnon-Roberge, A. A. C. M., Kern, R., Woodard, D., Cahill, B. S., & Ebner, N. C. (2026) · Cognitive Research Principles and Implications · doi:10.1186/s41235-025-00700-y · Cited by 16
“…eye movements (Gupta et al., 2020), head poses (Yang et al., 2018), consistency of facial geometry (Tursman et al., 2020), facial expressions (Agarwal et al., 2020), lip sync-ing (Korshunov & Marcel, 2021), and biological signals from facial regions (e.g., photoplethysmography, head…”
Multi-source Deep Domain Adaptation for Deepfake Detection
Seraj, M. S., & Chakraborty, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78305-0_9 · Cited by 4
Deepfake Detection: Emerging Techniques and Evolving Challenges
Natarajan, K., Mathur, A. K., Bhargava, K., Singh, M., & Tejpal, M. (2024) · International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON) · doi:10.1109/iemecon62401.2024.10846677 · Cited by 4
Face Omron Ring: Proactive defense against face forgery with identity awareness
Dai, Y., Fei, J., Huang, F., & Xia, Z. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106639 · Cited by 4
Learning Meta Model for Strong Generalization Deepfake Detection
Huang, D., & Zhang, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651482 · Cited by 6
“To counter the constraints of CNN-based methods, recent studies have delved into exploiting temporal inconsistencies in deepfake videos [13], [29], [33], [34].”
MethodEnhancing Digital Crime Investigation Through Deep Learning-Powered Deepfake Detection
Bhaskar, N., Tupe-Waghmare, P., Ganesh, S., Vinay, D., Leena, K., & Dsouza, D. (2024) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon62057.2024.10837976 · Cited by 4
“Although deepfake technology has lots of good applications, it also poses considerable concerns, including misinformation, privacy breaches, and security risks [1].”
Noise-Coded Illumination for Forensic and Photometric Video Analysis
Michael, P., Hao, Z., Belongie, S., & Davis, A. (2025) · ACM Transactions on Graphics · doi:10.1145/3742892 · Cited by 4
“For example, training a prior on a particular individual’s behavior [Agarwal et al. 2020; Cozzolino et al. 2023], or on statistical signs that an image is the result of compositing [Huh et al. 2018; Wang et al. 2019].”
SiamLBP: Exploiting Texture Discrepancies for Deepfake Detection
Kingra, S., Aggarwal, N., & Kaur, N. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0085-5_36 · Cited by 5
Comparing Human and Machine Deepfake Detection with Affective and Holistic Processing.Preprint
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Deepphysio: detecting deepFake with non-personalized feature of physiological signal
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“To overcome reliance on the original pixel domain and improve the reliability and robustness of detection systems, Agarwal et al. [26] proposed a detection system that combines static biological features and dynamic behavioral features.”
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RAIRNet: Region-Aware Identity Rectification for Face Forgery Detection
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“With the observation of inevitable identity deviation in forged images [4, 12, 16, 18, 44], identity-based methods are proposed to verify whether the suspect query image preserves unified identity traits with involved subject.”
BackgroundMethodInfluentialConvolutional 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
“[11]”
ExDDV: A New Dataset for Explainable Deepfake Detection in Video
Hondru, V., Hogea, E., Onchiş, D., & Ionescu, R. T. (2026) · 2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv61042.2026.00416 · Cited by 3
“Early methods for deepfake detection in video were based on convolutional networks [2, 3, 18, 24, 25, 27, 32, 44, 53, 56, 68, 82].”
"My face, my rules": Enabling Personalized Protection Against Unacceptable Face Editing
Xiao, Z., Cryan, J., Yao, Y., Cheo, Y. H. G., Shu, Y., Saroiu, S., Zhao, B. Y., & Zheng, H.-T. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0080 · Cited by 2
“(3) Inspecting image visual content: Many propose to detect face edits by studying image visual content and identifying digital artifacts introduced during face editing [4, 34].”
BackgroundEnhancing DeepFake Detection: Leveraging Mesonet for Video Fraud Identification
Shanmuganathan, C., Thamizharasi, M., Anish, T. P., & Sivasankari, K. (2024) · SN Computer Science · doi:10.1007/s42979-024-02629-3 · Cited by 3
“To perform all these process in a fixed successful way the model is under the requirement of a large amount of data that are needed to generate realistic facial characteristics features for the DeepFake creation [4].”
Detecting Deepfake Images: A Deep Learning Approach with Streamlit Integration
Gadde, A., Kishore, G., Talari, T., Nunna, S. L., Nannapaneni, R. C., & Vamsi, K. M. (2024) · doi:10.1109/icstem61137.2024.10560646 · Cited by 4
Security Strengthen and Detection of Deepfake Videos and Images Using Deep Learning Techniques
Talreja, S., Bindle, A., Kumar, V., Budhiraja, I., & Bhattacharya, P. (2024) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops59551.2024.10615811 · Cited by 4
“Early research on Deepfake detection centered on physical examination and fundamental forensic methods to spot visual artefacts and discrepancies in altered media [3].”
BackgroundCountering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models
Mathur, A. K., Bhargava, K., Singh, M., Tejpal, M., & Natarajan, K. (2024) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict61591.2024.10718622 · Cited by 3
“…et al. [11] and Hasan et al. [12] There are also methods like Biometric and behavioral analysis, such as the ones carried out by Agarwal et al. [13] and Durall et al. [14] Advanced pattern recognition showcases the true potential of CNNs as shown in a study by Nirkin et al. [15], this system…”
MethodDeepFake Detection with Inconsistent Head Poses: Reproducibility and AnalysisPreprint
Lutz, K., & Bassett, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12715 · Cited by 13
“In follow-up work [7], the authors use a convolutional network to capture facial mannerisms, which avoids the labor-intensive process of hand-crafting the correlation-based features.”
InfluentialResNeXt-50 and LSTM for Deepfake Detection
Kamil, F., Maharani, E. M., & Rahmania, R. (2025) · IEEE International Conference on Artificial Intelligence for Learning and Optimization (ICoAILO) · doi:10.1109/icoailo66760.2025.11156046 · Cited by 3
“…98.68 Face Cut-Out2 [36] 92.00 84.00 CNNs and Transformers [37] 98.25 94.84 AlexNet [38] 91.32 98.85 MesoNet + Preprocessing module [39] 94.10 93.60 ResNet model with the Vision Transformer [40] 92.06 93.27 ResNet18 [41] 68.83 92.23 ResNet 101 and VGG Using appearance and behavior [42] 93.30 98.”
InfluentialDomain-Adaptive Representation Learning for Multimodal Deepfake Detection
Haq, I. U., & Malik, K. M. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3637242 · Cited by 2
“…of DARL on datasets featuring political figures, specifically WRLD and PDD. Table II presents a comparative analysis of five state-of-the-art methods, including graph neural networks [82], Neurosym-bolic [23], behavior biometrics [81], and artifact-based meth-ods on these datasets [73], [74].”
SynthMed: Generating and Detecting Multimodal Deepfakes for Healthcare Communication
Barone, M., Serio, F. D., Moscato, V., Postiglione, M., Riccio, G., & Romano, A. (2025) · International Symposium on Multimedia · doi:10.1109/ism66958.2025.00034 · Cited by 2
“…or photometric inconsistencies, such as the discrepancy of light reflections in the eyes proposed by the University of Buffalo [15], while others rely on biometric signatures such as facial micro-expressions, head movements or blink patterns that are difficult to reproduce synthetically [16].”
Computer Vision and Building Resilience Against Online Violent Extremism in Africa
Mose, W. O. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-6684-8127-1.ch003 · Cited by 2
Deepfakes: A New Kind of Adversarial Attacks Against Face Recognition Systems?
Frick, R. A., & Graner, L. (2024) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom63139.2024.00341 · Cited by 2
“These techniques either utilize handcrafted features within model-based detection schemes [27], [28] or exploit features automatically extracted from training data through deep learning in data-driven detection schemes [20], [29].”
Arribas, C. M., Arcos, R., & Gertrudix, M. (2025) · MethodsX · doi:10.1016/j.mex.2025.103401 · Cited by 2
“Among the existing approaches, the development of detection models [ 1–3 ] for this type of content requires continuous adaptation to a rapidly changing ecosystem, leading to a cat-and-mouse game dynamic [ 4 ].”
Die Rolle Künstlicher Intelligenz in Assistiven Technologien
May, S., Szczypien, N., Vahldiek, K., & Klawonn, F. (2022) · Gesundheit. Politik - Gesellschaft - Wirtschaft · doi:10.1007/978-3-658-34027-8_4 · Cited by 2
Assessing YOLOv8 as a Classifier for Detection of Synthetically Generated Facial Imagery
Sharma, V., Singh, N., & Prasad, R. (2024) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin60856.2024.10511616 · Cited by 2
“Another technique was given by Agrawal et al. [4] for detecting face-swap, deepfakes combines a behavioral biometric approach with static facial recognition.”
MethodHydara, E., Kikuchi, M., & Ozono, T. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-64650-8_10 · Cited by 2
NWT: Towards natural audio-to-video generation with representation learningPreprint
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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
Evasion on general GAN-generated image detection by disentangled representation
Chan, P. P. K., Zhang, C., Chen, H., Deng, J., Meng, X. J., & Yeung, D. S. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121267 · Cited by 1
Deepfake Attacks on Biometric Recognition: Evaluation of Resistance to Injection Attacks
Vijaykumar, R., Purnapatra, S., Plesh, R. O., Imtiaz, M. H., Hou, D., & Schuckers, S. (2024) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon62879.2024.10770760 · Cited by 1
Media Forensics and Deepfake-Systematic Survey
Nadeem Jabbar, C. H., Saghir, A., Meer, A. A., Sahi, S., Hassan, B., & Yasir, S. M. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-77617-5_24 · Cited by 6
Incorporating Psychological and Behavioral Cues for Enhanced Deepfake Detection
Agrawal, P., Shaikh, A., Bajaj, K., & Mahajan, J. (2025) · OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry (OTCON) · doi:10.1109/otcon65728.2025.11071123 · Cited by 1
“Modern detection strategies now incorporate temporal dynamics [1], utilizing inconsistencies in movement, voice [7], and behavior [5], [6] to enhance accuracy.”
Yang, G., Zhu, P., Zhang, J., & Yang, X. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07795-6 · Cited by 1
Swin-BiLSTM with Attention for Face Swap Detection in Deepfake Videos
Lukitania, N. F., & Suryani, V. (2025) · doi:10.1109/icoict66265.2025.11192953 · Cited by 1
“However, its potential misuse for criminal purposes, including the spread of fake news and manipulation of an individual’s image, raises serious privacy and security concerns [1].”
The Role of Self‐Efficacy, Critical Thinking, and Media Literacy in Human Deepfake Video Detection
Chen, C., & Goh, D. H. (2025) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1310 · Cited by 2
“It is important to study the deepfake context because, unlike textual or image-based misinformation, detecting deepfakes requires understanding complex visual, auditory, and behavioural cues (Agarwal et al., 2020; Cooke et al., 2025; Groh et al., 2022).”
Choose Your Expert: Uncertainty-Guided Expert Selection for Continual Deepfake Detection
Zhang, X., Zhu, P., Sui, J., Yang, X., Tian, J., Lao, M., Cai, S., Guo, Y., & Tang, J. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755398 · Cited by 5
“Early detection methods mainly relied on handcrafted features or abnormal physiological signals, such as eye blinking frequency [2, 24, 28, 36, 88], head pose [1, 62, 77, 77], and pupil shape [21].”
Deepfake Detection in Visual Media Using ResNext Feature Extraction and LSTM Classification
Sharma, N., Narula, R., Balyan, D., Kumar, V., Vashishtha, S., & Chaudhary, P. (2025) · International Conference on Intelligent Systems for Cybersecurity (ISCS) · doi:10.1109/iscs69371.2025.11386378 · Cited by 1
“The manipulated videos are generated by means of the Style-Based Generator Architecture of GANs used by NVIDIA that allows modifying facial attributes with the help of advanced deep learning techniques, creating a highly realistic synthetic content [2][10].”
Jahan, F., Shifat, S. M., Morol, M. K., Fahad, N., Goh, K. O. M., Nandi, D., Abdullah-Al-Jubair, M., & Hossen, M. J. (2026) · Discover Applied Sciences · doi:10.1007/s42452-026-08402-w · Cited by 2
Raza, S., Ho, J., Raza, M., & Emmanouilidis, C. (2026) · ACM International Workshop on Multimedia AI against Disinformation (MAD) · doi:10.1145/3810988.3812659 · Cited by 1
“…as blink dynamics, pulse-induced skin-colour variation, and micro-expression timing; the corresponding forensic signals are remote-photoplethysmography (rPPG) traces [15, 25] and blink-interval statistics [41], and these cues are the most durable detection dimen-sion but remain under-exploited [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
“Deepfake audio is synthetic audio, generated by Artificial Intelligence (AI) models, through manipulation of pre-existing audio data [1].”
سلوك حمایة الخصوصیة الرقمیة البیومتریة لدى مستخدمی تطبیقات التزییف العمیق من طلبة الجامعات المصریة
دياب, م. ا. ك. م. (2022) · المجلة العربية لبحوث الاعلام والاتصال · doi:10.21608/jkom.2022.253088 · Cited by 1
البيومى, ر. ا. ع. ا. (2023) · Maǧallaẗ Rūḥ Al-Qawānīn /Maǧallaẗ Rūḥ Al-Qawānīn · doi:10.21608/las.2023.343833 · Cited by 1
She, H., Hu, Y., Liu, B., & Li, C. (2024) · IET Biometrics · doi:10.1049/2024/2280143 · Cited by 1
Kompetenzen kommunikativen Handelns im Kontext mediatisierter Digitalität
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115 · Cited by 2
Making Original 2D Visual Deepfakes With a Generative AI Tool
Hai-Jew, S. (2024) · Advances in multimedia and interactive technologies book series · doi:10.4018/979-8-3693-2927-6.ch006 · Cited by 1
The Use of Deep Fakes in Social Engineering Attacks
Saxena, A., & Grewal, H. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-6665-3.ch013 · Cited by 1
Pandian, H., & Rawindaran, N. (2024) · Springer eBooks · doi:10.1007/978-3-031-76473-8_3 · Cited by 3
Evaluating Deepfake Applications in Biometric Technology – A Review
Khan, S., & Praveen, B. M. (2025) · International Journal of Applied Engineering and Management Letters · doi:10.47992/ijaeml.2581.7000.0231 · Cited by 1
Double-Stream Segmentation Network with Temporal Self-attention for Deepfake Video Detection
Liu, B., Gao, Y., Hu, Y., Guo, J., & Wang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95398-0_3
Contrastive Knowledge Transfer for Deepfake Detection with Limited Data
Li, D., Zhuo, W., Wang, W., & Dong, J. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956333
“Some of the deepfake detection methods focus on different hints left by tampered images and videos, such as abnormal person appearances, inconsistent contexts and behaviors [8], [9], [10], [11], temporal/spatial inconsistency [12] and signal level artifacts [13], [14].”
BackgroundLyubykh, Z., Barclay, L. J., Fortin, M., Bashshur, M. R., & Khakhar, M. (2022) · Research in Organizational Behavior · doi:10.1016/j.riob.2023.100183
MesoNet3: A Deepfakes Facial Video Detection Network Based on Object Behavior Analysis
Jaleel, Q., & Ali, I. H. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-35442-7_3
Hybrid Model for Detecting Deepfake Videos
Ramachandra, A. C., Nihal, K. P., Mishra, K., Mohana Sahithi, P., Prajwal, A. V., & Jagadeesh, H. S. (2023) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns58469.2023.10245432
"توظيف صور المشاهير باستخدام تقنية التزييف العميق في إعلانات اليوتيوب ودلالة هذه الصور"
السلام, س. م. ا. م. ع. (2023) · Al-Maǧallah Al-Miṣriyyaẗ Li-Buḥūṯ Al-Iʿlām · doi:10.21608/ejsc.2023.343327
Detecting Deepfake Videos Dilemma Revisited: Finding Gaps and Shortcomings
Kharbat, F. F., & Badawi, L. (2023) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit58888.2023.10453729
Deepfakes in Videoverhandlungen vor Gericht
Steffes, B., & Zichler, A. (2024) · Datenschutz und Datensicherheit - DuD · doi:10.1007/s11623-023-1899-1
“Schließlich können untypische Verhaltensmuster oder Ge-sichtsausdrücke ein Hinweis auf eine Veränderung von Video-material sein [27].”
SHIELD: A Specialized Dataset for Hybrid Blind Forensics of World Leaders
Lin, Q., Li, X., Chu, B., Wang, R., Chen, X., Mao, Y., Yang, Z., Zhou, L., & You, W. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-56583-0_6
Advanced Deepfake Detection Using Honey Badger Optimization and Elm ClassifierPreprint
Quadir, M., & Agrawal, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4831545
Applying Self-recognition Biometrics to Live Deepfake Detection in Video Conferences
Graupner, H., Thobae, F.-N., & Meinel, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-61489-7_18
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-013
4. Kompetenzen kommunikativen Handelns in der Lehrkräftebildung
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-009
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-012
6. Zusammenführende Betrachtung und Diskussion
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-011
3. Kommunikative Wirklichkeitskonstruktion im Kontext mediatisierter Digitalität
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-008
5. Modellierung der Kompetenzen kommunikativen Handelns
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-010
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-004
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-003
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-006
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-005
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-007
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-002
Watolla, A.-K. M. (2024) · Digitale Gesellschaft · doi:10.14361/9783839473115-toc
Metadata
- Authors
- Agarwal, S., Farid, H., El-Gaaly, T., & Lim, S.-N.
- Affiliations
- University of California, Berkeley (US)
- Meta (United States) (US)
- Venue
- 2020 IEEE International Workshop on Information Forensics and Security (WIFS)
- Publisher
- IEEE
- Published
- 2020-12-06
- Pages
- 1-6
- ISSN
- 2157-4774 (ISSN-L)
- OpenAlex
- W3021589373
- Cited by
- 242
- References
- 39
- Citations by year
- 2026: 26 · 2025: 39 · 2024: 79 · 2023: 36 · 2022: 36 · 2021: 22 · 2020: 4
- FWCI
- 7.56
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Workshop on Information Forensics and Security (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- 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
- deepfake detection, face recognition, facial expressions, head movement, behavioral biometrics, metric learning, disinformation
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