Improving Generalization in Facial Manipulation Detection Using Image Noise Residuals and Temporal Features
Open accessAbstract
The high visual quality of modern deepfakes raises significant concerns about the trustworthiness of digital media and makes facial tampering detection more challenging. Although current deep learning-based deepfake detectors achieve excellent results when tested on deepfake images or image sequences generated using known methods, generalization—where a trained model is tasked with detecting deepfakes created with previously unseen manipulation techniques—is still a major challenge. In this paper, we investigate the impact of training spatial and spatio-temporal deep learning network architectures in the image noise residual domain using spatial rich model (SRM) filters on generalization performance. To this end, we conduct a series of tests on the manipulation methods of the FaceForensics++, DeeperForensics-1.0 and Celeb-DF datasets, demonstrating the value of image noise residuals and temporal feature exploitation in tackling the generalization task.
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SRM high-frequency filter blocks are efficient and effective at improving results on the generalization task.
“In [3], these filters are shown to be efficient and effective at improving results on the generalization task.”
From WaveConViT: Wavelet-Based Convolutional Vision Transformer for Cross-Manipulation Deepfake Video Detection (2025) · cites this paper as [3]
High-frequency filtering suppresses meaningful temporal manipulation artifacts such as color inconsistency between successive frames or flickering artifacts in deepfake videos.
“This reinforces the findings in [3], where high-frequency filtering was shown to suppress meaningful temporal manipulation artifacts such as color inconsistency between successive frames in a deepfake video or flickering artifacts.”
From WaveConViT: Wavelet-Based Convolutional Vision Transformer for Cross-Manipulation Deepfake Video Detection (2025) · cites this paper as [3]
Frequency-aware deepfake detection works better when learning occurs on a diverse set of tampering artifacts.
“As shown in [3], frequency-aware deepfake detection works better when learning occurs on a diverse set of tampering artifacts.”
From WaveConViT: Wavelet-Based Convolutional Vision Transformer for Cross-Manipulation Deepfake Video Detection (2025) · cites this paper as [3]
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Self-citation
- Authors
- Atamna, M., Tkachenko, I., & Miguet, S.
- Affiliations
- Lyon 1 Université (FR)
- Venue
- 2023 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2023-10-08
- Pages
- 3424-3428
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W4386590367
- Cited by
- 8
- References
- 19
- Citations by year
- 2026: 2 · 2025: 5 · 2023: 1
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- green
- hal.science
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- HAL (submittedVersion, other-oa)
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- deepfake detection, generalization, temporal features, spatio-temporal networks, FaceForensics++, Celeb-DF
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