Region-Aware Temporal Inconsistency Learning for DeepFake Video Detection
Authors: Zhihao Gu, Taiping Yao, Yang Chen, Ran Yi, Shouhong Ding, Lizhuang Ma
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and visualizations on several benchmarks demonstrate the effectiveness of our method against state-of-the-art competitors. |
| Researcher Affiliation | Collaboration | 1School of Electronic and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China 2Tencent Youtu Lab, Shanghai, China 3Mo E Key Lab of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | No | The paper describes the proposed methods and architecture using text and diagrams, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | To evaluate the proposed method, we perform state-of-the-art comparison under intra-dataset and cross-dataset generalization settings on four popular benchmarks: Face Forensics++ (FF++) [Rossler et al., 2019], Celeb-DF [Li et al., 2020c], DFDC [Dolhansky et al., 2019], Wild Deepfake [Zi et al., 2020]. |
| Dataset Splits | No | The paper describes training parameters and testing on specific datasets but does not explicitly detail the training/validation/test dataset splits, such as specific percentages or sample counts for a validation set. |
| Hardware Specification | No | The paper mentions '8 GPUs' but does not specify the exact GPU models, CPU types, or other detailed hardware specifications used for the experiments. |
| Software Dependencies | No | The paper mentions the use of ResNet-50 as a backbone and the Adam algorithm, but it does not specify version numbers for any programming languages, libraries, or frameworks (e.g., Python version, PyTorch/TensorFlow version). |
| Experiment Setup | Yes | During training, we sample U = 4 snippets and each snippet contains T = 4 frames. And images are resized to 224 224 as input to the network. The temperature scalar is set as 10 2. We adopt the Adam algorithm to optimize the binary cross-entropy loss and train the network for 45 epochs on 8 GPUs with the initial learning rate of 10 4. The learning rate is divided by 10 for every 15 epochs and batch-size is 12. Random horizontal flip is employed as the data augmentation. During inference, eight 4-length snippets are centrally sampled from each segment. |