Dual Contrastive Learning for General Face Forgery Detection
Authors: Ke Sun, Taiping Yao, Shen Chen, Shouhong Ding, Jilin Li, Rongrong Ji2316-2324
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and visualizations on several datasets demonstrate the generalization of our method against the state-of-the-art competitors. |
| Researcher Affiliation | Collaboration | Ke Sun1, Taiping Yao2, Shen Chen2, Shouhong Ding2 , Jilin Li 2, Rongrong Ji1 1Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, 361005, China 2Youtu Lab, Tencent, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our Code is available at https://github.com/Tencent/TFace.git. |
| Open Datasets | Yes | To evaluate our method, we conduct experiments on five famous challenging datasets: Face Forensics++ (Rossler et al. 2019), Celeb DF (Li et al. 2019b), DFDC (Dolhansky et al. 2020), DFD, Wild Deepfake (Zi et al. 2020). |
| Dataset Splits | Yes | Face Forensics++ (Rossler et al. 2019) is a large-scale forgery face dataset containing 720 videos for training and 280 videos for validation or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'Efficient Net-b4' but does not provide specific version numbers for software dependencies or programming frameworks (e.g., PyTorch 1.9, Python 3.8). |
| Experiment Setup | Yes | The learning rate is set to 0.001 and the batchsize is set to 32. The Efficient Net-b4 (Tan and Le 2019) pretrained on the Image Net (Deng et al. 2009) is used as our encoders fq and fk. The exponential hyper-parameter β is set to 0.99. The temperature parameter τ of E.q. 3 is set to 0.07 and the query size |M| is set to 30000. In addition, we set 0.9 and 0.5 for prototypes updating parameter α and threshold θ. For the balanced weight φ, we set φ = 0.1 for the first 5 epochs as the warm-up period under the guidance of lce, then the φ is set to 0.5. |