Authors : Dr. Chayadevi M. L.; Harsha Bharadwaj; Guru Raj Patil; Keerthana K.

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/mvnz2n2s

DOI : https://doi.org/10.38124/ijisrt/26sep1010

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Recent progress in generative deep learning—progressing from early autoencoders and GANs to modern latent diffusion models and neural vocoders—have made synthetic media generation accessible, realistic, and increasingly difficult to identify. While these tools offer productive uses in creative production and accessibility, they create serious security risks through identity fraud, automated disinformation, and unauthorized voice and video impersonation. In response, forensic detection has developed from simple pixel-level artifact checks into complex multimodal pipelines. This survey provides an organized, critical review of current deepfake detection techniques across image, video, audio, and hybrid modalities. We examine the core architectures used in detection—such as Convolutional Neural Networks, Recurrent Networks, Vision Transformers, graph-based phoneme models, and acoustic reverberation estimators—and analyze their behavior on standard benchmarks including FaceForensics++, Celeb-DF, DFDC, ASVspoof, and FakeAVCeleb. We give particular attention to cross-dataset generalization failures, practical latency constraints for on-device deployment, and the growing need for multimodal fusion to maintain detection reliability against next-generation generative models.

Keywords : Deepfake Detection; Digital Media Forensics; Convolutional Neural Networks; Vision Transformers; Audio Forensics; Speech Synthesis Detection; Cross-Dataset Generalization; Direct-to-Reverberant Ratio; Multimodal Fusion.

References :

1. A. Heidari, N. J. Navimipour, H. Dag, and M. Unal, “Deepfake detection using deep learning methods: A systematic and comprehensive review,” Arch. Comput. Methods Eng., vol. 31, no. 3, pp. 1903–1936, 2024.
2. G. Pei, J. Zhang, M. Hu, Z. Zhang, C. Wang, Y. Wu, G. Zhai, J. Yang, C. Shen, and D. Tao, “Deepfake generation and detection: A benchmark and survey,” IEEE Trans. Pattern Anal. Mach. Intell., early access, 2025.
3. S. A. Khan and D.-T. Dang-Nguyen, “Deepfake detection: A comparative analysis,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 20, no. 3, Art. no. 75, pp. 1–28, 2024.
4. K. Patil, S. Kale, J. Dhokey, and A. Gulhane, “Deepfake detection using biological features: A survey,” in Proc. Int. Conf. Advances in Computing, Communication and Control (ICAC3), 2023, pp. 1–7.
5. T. T. Nguyen, Q. V. H. Nguyen, D. T. Nguyen, T. Huynh-The, S. Nahavandi, T. T. Nguyen, Q.-V. Pham, and C. M. Nguyen, “Deep learning for deepfake creation and detection: A survey,” Comput. Vis. Image Underst., vol. 223, Art. no. 103525, 2022.
6. A. Hashmi, S. A. Shahzad, C.-W. Lin, Y. Tsao, and H.-M. Wang, “Understanding audiovisual deepfake detection: Techniques, challenges, and human factors,” IEEE Trans. Multimedia, vol. 26, pp. 10582–10599, 2024.
7. K. Zhang, Z. Hua, R. Lan, Y. Zhang, and Y. Guo, “Phoneme-level feature discrepancies: A key to detecting sophisticated speech deepfakes,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2024, pp. 4150–4154.
8. R. Kundu, H. Xiong, V. Mohanty, A. Balachandran, and A. K. Roy-Chowdhury, “Towards a universal synthetic video detector: From face or background manipulations to fully AI-generated content,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2024, pp. 1–11.
9. G. Lee, J. Lee, M. Jung, J. Lee, K. Hong, S. Jung, and Y. Han, “Dual-channel deepfake audio detection: Leveraging direct and reverberant waveforms,” IEEE Access, vol. 12, pp. 142105–142118, 2024.
10. Z. Xie and J. Luo, “Shaking the fake: Detecting deepfake videos in real time via active probes,” in Proc. 32nd ACM Int. Conf. Multimedia (MM ’24), 2024, pp. 1095–1104.
11. D. Nguyen, M. Astrid, E. Ghorbel, and D. Aouada, “FakeFormer: Efficient vulnerability-driven transformers for generalisable deepfake detection,” in Proc. European Conf. Computer Vision (ECCV), 2024, pp. 1–18.
12. Y. Chen, N. Akhtar, A. Mian, and N. A. H. Haldar, “Deepfake detection with spatio-temporal consistency and attention,” IEEE Trans. Inf. Forensics Security, early access, 2025.
13. R. Corvi, D. Cozzolino, G. Zingarini, G. Poggi, K. Nagano, and L. Verdoliva, “On the detection of synthetic images generated by diffusion models,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2023, pp. 1–5.
14. H. Khalid, S. Tariq, M. Kim, and S. S. Woo, “FakeAVCeleb: A novel audio-video multimodal deepfake dataset,” in Proc. 35th Conf. Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2021, pp. 1–12.
15. B. Zi, M. Peng, J. Wang, B. Chen, X. Wang, and B. Xiao, “WildDeepfake: A challenging real-world dataset for deepfake detection,” in Proc. 28th ACM Int. Conf. Multimedia (MM ’20), 2020, pp. 2382–2390.
16. Y. Ju, S. Jia, J. Ke, Y. Yao, and D. Patel, “Fusing multi-scale spatio-temporal features for audio-visual deepfake detection,” IEEE Trans. Circuits Syst. Video Technol., vol. 34, no. 6, pp. 4812–4825, 2024.