The development of deepfake production, which has produced audio-visual frauds of unprecedented realism, has been fueled by recent innovations such as lip synchronizing, neural face reenactment, and synthetic speech. As forensic evidence, low-level visual artifacts and evident audio distortions are becoming less trustworthy, and the identification is becoming more reliant on the fundamentals of human speech, which is multimodal and has strong temporal, articulatory, and semantic links between lip movements and acoustic phonemes. Consequently, audio-visual integration offers a fair forensic indicator that is concep-tually sound and mostly consistent across recording settings, languages, and speakers. The study explores the application of synchronicity-based methods to identify deepfakes in phys-iological coherence modeling, zero-shot semantic consistency analysis, transformer-based multimodal fusion, supervised and self-supervised deep learning, and traditional signal process-ing. We utilize syncnet-style lip-audio matching, AVTS and AV-HuBERT self-supervision, ASR-VSR semantic matching, cross-modal focus networks, and historical and current research in addition to novel lip-sync-specific sensors. genuine datasets, evaluation methods, robustness, real-world applications, and generalization patterns Logistical challenges also need to be taken into account.
Analysis of audio-visual synchronization is arguably the most progressive and technically grounded approach in detecting deepfakes. While artifact detection uses weaknesses of the model to find inconsistencies, synchronization is based on cross-modal limitations, such as the close connection between speech production components, including voice, articulation movement, facial expression, and semantics, that is rooted in human physiology. Although current deep learning models can generate increasingly realistic images and voices, the rela-tionships between these elements provide a more solid founda-tion for a detection process due to the identical physical/neural principles behind their interactions. Recent advances in the field were discussed in the article under review in several re-search directions regarding different synchronization signals: temporal alignment, representational-level cross-modal consis-tency, cross-modal transformers, semantic agreement of auto-matic speech recognition and visual speech recognition sys-tems, and physiological cues. As per the findings of the sur-vey, the development of the area was logical: first, initial align-ment techniques showed their feasibility, then increasingly so-phisticated supervised temporal deep models emerged, and fi-nally, self-supervised and multi-modal transformer-based mod-els were developed. One takeaway from this survey is that forensic synchronization should be implemented through multi-constraint models instead of simple score methods. Overall, multi-constraint models that consider factors such as short-term alignment, long-term consistency, semantics, and modality re-liability estimation tend to outperform one-dimensional metrics regarding compression tolerance, occlusion, and adaptation to different domains. Thus, for a viable forensic synchronization system, proper calibration and uncertainty handling are essen-tial but not only technical challenges. Nevertheless, several lim-itations should be mentioned. Compression, a low frame rate, mouth occlusion, and the multi-language nature of visemes may negatively impact synchronization. It is necessary to establish whether the discrepancy between modalities stems from inten-tional modification or simply reflects inter-modal inconsisten-cies due to dubbing and voice-over modifications. The avail-able benchmarking efforts exhibit a preference toward certain languages and data collection procedures. Currently, there is no universal stress test for forensic synchronization.The sys- temic perspective underscores the significance of having well-functioning pipelines, precise monitors, and meaningful results. In instances that require justifications, synchronizers are partic-ularly useful since they will make evident the points at which synchronization was not achieved, at which words do not match across modalities, and the sections of the speech that were highly uncertain in their synchronization. These are essential in contexts such as forensics, content moderation, and journal-ism, among others, since justifications will be required for the actions taken as part of the processes involving the AI-driven functions. Going forward, the key focus should be on using large multimodal foundation models with forensic considera-tions.With multilingual data, thorough evaluations on different data sets, uncertainty-aware integration, and robustness metrics for standardization, this method is capable of generating detec-tors that will maintain their effectiveness even as the generators become more advanced. Finally, synchronization-based detec-tion of deepfakes is an idea of multimodal verification, a uni-versal principle, and not a technology alone. If the methods developed for deepfake detection involve cross-modal speech synchronization rather than abnormality recognition, this field will move toward more robust detection techniques that are also more interpretable and transferable and resilient against gener-ator updates.
References
[1]M. F. Hashmi, S. A. Shahzad, C.-W. Lin, Y. Tsao, and H.-M. Wang, “A comprehensive survey of audio-visual deepfake generation and detection techniques,” arXiv:2411.07650, 2024. [2]Z. Zhang and S. Li, “Joint audio-visual deepfake detec-tion,” in Proc. IEEE ICCV Workshops, 2021. [3]J. S. Chung and A. Zisserman, “Out of time: Automated lip-sync in the wild,” in Proc. Asian Conf. Computer Vi-sion (ACCV), 2016. [4]K. R. Prajwal, R. Mukhopadhyay, V. Namboodiri, and C.
V. Jawahar, “Wav2Lip: Accurately lip-syncing videos in the wild,” in Proc. ACM Multimedia, 2020. [5]B. Korbar, D. Tran, and L. Torresani, “Cooperative learn-ing of audio and video models from self-supervised syn-chronization,” in Proc. NeurIPS, 2018. [6]B. Shi et al., “AV-HuBERT: Self-supervised learning of audio-visual speech representation,” in Proc. NeurIPS, 2022. [7]A. Rössler et al., “FaceForensics++: Learning to detect manipulated facial images,” in Proc. IEEE ICCV, 2019. [8]B. Dolhansky et al., “The DeepFake Detection Challenge (DFDC) dataset,” arXiv:2006.07397, 2020. [9]L. Verdoliva, “Media forensics and deepfakes: An overview,” IEEE Journal of Selected Topics in Signal Pro-cessing, vol. 14, no. 5, pp. 910–932, 2020.
[10]Y. Mirsky and W. Lee, “The creation and detection of deepfakes: A survey,” ACM Computing Surveys, vol. 54, no. 1, 2021.
[11]R. Tolosana et al., “Deepfakes and beyond: A survey of face manipulation and fake detection,” Information Fu-sion, vol. 64, pp. 131–148, 2020.
[12]J. Thies, M. Zollhöfer, M. Stamminger, C. Theobalt, and
M. Nießner, “Face2Face: Real-time face capture and reen-actment of RGB videos,” in Proc. IEEE CVPR, 2016.
[13]M. Datta, Y. Jia, and S. Lyu, “Detecting lip-syncing deep-fakes using vision temporal transformers (LIPINC-V2),” arXiv:2504.01470, 2025.
[14]X. Liu et al., “Lips are lying: Spotting the temporal in-consistency between audio and visual in lip-syncing deep-fakes,” arXiv:2401.15668, 2024.
[15]Y. Li et al., “Zero-shot fake video detection via ASR–VSR semantic consistency,” arXiv:2406.07854, 2024.
[16]M. Bohácˇek and H. Farid, “Lost in translation: Lip-sync deepfake detection from audio-video mismatch,” in Proc. IEEE/CVF CVPR Workshops, 2024.
[17]R. Kharel, W. Cai, and K. Radecka, “DF-TransFusion: Multimodal deepfake detection via lip-audio cross-attention and facial self-attention,” arXiv:2309.06511, 2023.
[18]S. Khan et al., “AV-Lip-Sync+: Leveraging AV-HuBERT for lip-sync deepfake detection,” arXiv:2311.02733, 2023.
[19]Y. Li, M.-C. Chang, and S. Lyu, “In Ictu Oculi: Exposing AI-created fake videos by detecting eye blinking,” in Proc. IEEE WIFS, 2018.
[20]A. A. El-Taj et al., “TrueSync: Deepfake detection based on visual lip-sync match and blink rate,” ResearchGate Preprint, 2025.
[21]M. Javed et al., “Audio–visual synchronization and lip movement analysis for real-time deepfake detection,” In-ternational Journal of Computational Intelligence Sys-tems, 2025.
[22]B. Dolhansky et al., “The DeepFake Detection Challenge dataset,” arXiv:1910.08854, 2019.
[23]Y. Li, X. Yang, P. Sun, H. Qi, and S. Lyu, “Celeb-DF: A large-scale challenging dataset for deepfake forensics,” in Proc. IEEE/CVF CVPR, 2020.
[24]
H. Khalid, S. Tariq, M. Kim, and S. S. Woo, “FakeAVCeleb: A novel audio-video multimodal deep-fake dataset,” in Proc. NeurIPS Datasets and Benchmarks Track, 2021.
[25]P. Korshunov and S. Marcel, “DeepFakeTIMIT: A dataset for deepfake detection,” Idiap Research Institute, 2018.
R. Chugh, P. Gupta, A. Dhall, and R. Subramanian, “Not made for each other—Audio-visual dissonance-based deepfake detection and localization,” in Proc. ACM Multimedia, 2020.
How to Cite This Paper
Halil Ibrahim Dursunoglu (2026). Audio-Visual Synchronization Analysis for Deepfake Detection: A Comprehensive Review. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
