Deepfake Detection Method Based on Spatio-Temporal Information Fusion
Source abstract
Abstract
As Deepfake technology continues to evolve, the distinction between real and fake content becomes increasingly blurred. Most existing Deepfake video detection methods rely on single-frame facial image features, which limits t... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Anomaly Detection Techniques and Applications · Digital Media Forensic Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
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1. Deepfake. [cited 2019 Oct 29]. Available from: http://github.com/deepfakes/faceswap.
2. Generative adversarial networks: an overview10.1109/MSP.2017.2765202 ↗
3. Kingma DP, Welling M. Auto-encoding variational Bayes. arXiv:1312.6114. 2013.
4. SemanticStyleGAN: learning compositional generative priors for controllable image synthesis and editing
5. Wang Y, Chen X, Zhu J, Chu W, Tai Y, Wang C, et al. HifiFace: 3D shape and semantic prior guided high fidelity face swapping. arXiv:2106.09965. 2021.10.24963/ijcai.2021/157 ↗
6. Review of deep network generative fake face detection methods
7. Exposing digital forgeries by detecting traces of resampling10.1109/TSP.2004.839932 ↗
8. Exploiting visual artifacts to expose deepfakes and face manipulations
9. Pishori A, Rollins B, van Houten N, Chatwani N, Uraimov O. Detecting Deepfake videos: an analysis of three techniques. arXiv:2007.08517. 2020.
10. Deepfake detection by analyzing convolutional traces
11. FakeCatcher: detection of synthetic portrait videos using biological signals
12. In ictu oculi: exposing AI-created fake videos by detecting eye blinking
13. Face X-ray for more general face forgery detection
14. A study on face anti-spoofing based on semi-supervised learning
15. Face forgery detection by 3D decomposition
16. Mesonet: a compact facial video forgery detection network
17. Chugh K, Gupta P, Dhall A, Subramanian R. Not made for each other Audio-visual dissonance-based deepfake detection and localization. arXiv:2007.08517. 2020.10.1145/3394171.3413700 ↗
18. Multi-branch deepfake detection algorithm based on fine-grained features
19. Capsule-forensics: using capsule networks to detect forged images and videos
20. Multi-task learning for detecting and segmenting manipulated facial images and videos
21. Deepfake detection based on discrepancies between faces and their context10.1109/TPAMI.2021.3093446 ↗
22. Multi-attentional deepfake detection
23. M2tr: multi-modal multi-scale transformers for deepfake detection
24. Keresh A, Shamoi P. Liveness detection in computer vision: transformer-based self-supervised learning for face anti-spoofing. arXiv:2406.138602024. 2024.10.1109/ACCESS.2024.3513795 ↗
25. Exposing deepfake videos by detecting face warping artifacts
26. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16 × 16 words: transformers for image recognition at scale. arXiv:2010.11929. 2020.
27. FakeApp. [cited 2025 Jan 1]. Available from: https://www.fakeapp.org/download.
28. SegNet: a deep convolutional encoder-decoder architecture for image segmentation10.1109/TPAMI.2016.2644615 ↗
29. DeepFaceLab. [cited 2023 Oct 3]. Available from: http://github.com/iperov/DeepFaceLab.
30. DFaker. [cited 2022 May 1]. Available from: http://github.com/dfaker/df.
31. A robust approach to multimodal deepfake detection10.3390/jimaging9060122 ↗
32. Detecting deepfakes: using CNN to identify manipulated visual media
33. Attention is all you need
34. Quo Vadis, action recognition? A new model and the kinetics dataset
35. FaceForensics++: learning to detect manipulated facial images
36. Celeb-DF: a large-scale challenging dataset for deepfake forensics
37. Motion magnified 3-D residual-in-dense network for deepfake detection10.1109/TBIOM.2022.3201887 ↗