FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos
Authors: Juan Hu, Xin Liao, Jinwen Liang, Wenbo Zhou, Zheng Qin951-959
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the performance of our method is promising in terms of in-dataset detection performance, detection efficiency, and cross-dataset detection performance in high-visualquality Deepfake videos. |
| Researcher Affiliation | Academia | 1The College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. 2CAS Key Laboratory of Electromagnetic Space Information, University of Science and Technology of China. |
| Pseudocode | Yes | Algorithm 1: The algorithm process of the proposed frame inference-based detection framework. |
| Open Source Code | No | The paper does not provide any specific links to a code repository or explicitly state that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The Face Forensics++ (FF++) (Rossler et al. 2019) dataset, the Celeb-DF dataset (Li et al. 2020), the Wild Deepfake dataset (Zi et al. 2020), and the DFDCpreview dataset(Dolhansky et al. 2019) are utilized to show the performance of FInfer. |
| Dataset Splits | No | The paper analyzes the impact of parameters 's' and 't' on detection accuracy on the Celeb-DF dataset, implying a form of validation. However, it does not explicitly provide information on train/validation/test splits as percentages, absolute counts, or references to predefined standard splits for overall model training and evaluation. |
| Hardware Specification | Yes | All experiments are conducted in Keras on NVIDIA Titan Xp. |
| Software Dependencies | No | The paper mentions software like FFmpeg, dlib, Keras, and Adam optimizer but does not specify version numbers for these dependencies, which would be necessary for reproducible setup. |
| Experiment Setup | Yes | The batch size is set as 8. In the training phase, we set the learning rate as 0.001, which will be divided by 5 when the accuracy plateaus. The Adam optimizer (Kingma and Ba 2014) is utilized to optimize the model. We set the default threshold, whose value is 0.5, to calculate binary accuracy. |