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..
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion
Authors: ke sun, Shen Chen, Taiping Yao, Hong Liu, Xiaoshuai Sun, Shouhong Ding, Rongrong Ji
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiment |
| Researcher Affiliation | Collaboration | 1 Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China. 2 Youtu Lab, Tencent, P.R. China. 3 Osaka University, Japan. |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code are available in https://github.com/skJack/DiffusionFake.git. |
| Open Datasets | Yes | Dataset. To evaluate the generalization ability of Diffusion Fake, we conduct experiments on several challenging datasets: (1) Face Forensics++ (FF++) [31]: (2) Celeb-DF [23]: (3) Deep Fake Detection (DFD): (4) DFDC Preview (DFDC-P) [8]: (5) Wild Deepfake [54]: (6) Diff Swap [6]: |
| Dataset Splits | No | The paper mentions following 'the data split strategy used in Face Forensics++ [31]' but does not explicitly state the train/validation/test percentages or sample counts for reproduction within its own text. |
| Hardware Specification | No | The paper does not specify the exact hardware used for training or inference, such as specific GPU or CPU models, or details about the computing environment. |
| Software Dependencies | Yes | During training, we utilize a pre-trained Stable Diffusion 1.5 model with frozen parameters. |
| Experiment Setup | Yes | Input images are resized to 224x224 pixels. We employ the Adam optimizer with a learning rate of 1e-5 and a batch size of 32. The model is trained for 20 epochs. The hyperparameters λs and λt are set to 0.7 and 1, respectively. |