Deepfake Video Detection via Facial Action Dependencies Estimation
Authors: Lingfeng Tan, Yunhong Wang, Junfu Wang, Liang Yang, Xunxun Chen, Yuanfang Guo
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superiority of our method against the state-of-the-art methods. ... Experiments Setups Datasets The training of our models is carried out on the Face Forensics++ (FF++) (R ossler et al. 2018) dataset. ... Ablation Study In this subsection, we conduct a series of ablation studies on different components of FADE. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science and Engineering, Beihang University, China 2 School of Artificial Intelligence, Hebei University of Technology, China 3 CNCERT/CC, Beijing, China 4 Zhongguancun Laboratory, Beijing, China |
| Pseudocode | No | The paper describes the method using equations and architectural diagrams, but does not provide explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | The training of our models is carried out on the Face Forensics++ (FF++) (R ossler et al. 2018) dataset. ... Celeb-DF (CDF) (Li et al. 2020c) and Deep Fake Detection (DFD) (Nick Dufour and Andrew Gully 2019). |
| Dataset Splits | Yes | For each video, four clips with the same number of frames are evenly sampled for training, validation and testing. |
| Hardware Specification | Yes | These models are trained on four RTX 3080s for approximately 10000 iterations and the best model is selected according to the evaluations on the validation set. |
| Software Dependencies | No | The paper mentions optimizers (Adam W) and a backbone network (Xception) but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | 8 continuous frames with an interval of 8 are sampled from the videos as the input. ... In the training process, the batch size is set as 4 (clips). The initial learning rate of MDGM is 2e-4, while the learning rate of the backbone is one fifth of it. The learning rates will be divided by 2 when the AUC score plateaus for 3 epochs. The models are optimized via Adam W (Loshchilov and Hutter 2017) optimizer with β1 = 0.9, β2 = 0.999, and ϵ = 0.001. |