Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deepfake Video Detection via Facial Action Dependencies Estimation
Authors: Lingfeng Tan, Yunhong Wang, Junfu Wang, Liang Yang, Xunxun Chen, Yuanfang Guo
AAAI 2023 | Venue PDF | 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. |