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..
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 | Venue PDF | 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 ef๏ฌciency, 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. |