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..
Critical Forgetting-Based Multi-Scale Disentanglement for Deepfake Detection
Authors: Kai Li, Wenqi Ren, Jianshu Li, Wei Wang, Xiaochun Cao
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results validate the efficacy of the proposed method. ... Extensive experiments on forgery datasets demonstrate that our proposed method outperforms the state-of-the-art methods in terms of generalization and robustness. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Sun Yat-sen University 2School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University 3National University of Singapore |
| Pseudocode | No | The paper describes the proposed method in prose and mathematical formulations (Equations 1-11) but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use Face Forensics++ (FF++) (Rossler et al. 2019) as the training dataset... Celeb-DF V2 (CDF) (Li et al. 2020b), Deep Fake Detection Challenge (DFDC) (Dolhansky et al. 2020) and Wild Deep Fake (WDF) (Zi et al. 2020). ... Efficient Net (Tan and Le 2019) pre-trained on Image Net (Deng et al. 2009). |
| Dataset Splits | Yes | The split setting for training and validation is the same as the initial dataset setting. |
| Hardware Specification | No | The paper discusses the software and training parameters (e.g., optimizer, batch size, learning rate) but does not provide specific details about the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions several methods and models used (Retina Face, Efficient Net, Adamw optimizer) but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | For training, we use a batch size of 256 and employ the Adamw optimizer with beats 0.9 and 0.999. The learning rate is set as 2e-4. The hyper-parameters λ1, λ2, λ3 and λ4 are set as 0.1, 0.15, 0.1, and 0.1, respectively. |