| --- |
| tags: |
| - deepfake |
| - detection |
| - robustness |
| --- |
| # Model Card for PMM |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| This is the model card for the PMM model from [Practical Manipulation Model for Robust Deepfake Detection](https://openaccess.thecvf.com/content/ICCV2025W/AIM/papers/Hopf_Practical_Manipulation_Model_for_Robust_Deepfake_Detection_ICCVW_2025_paper.pdf) |
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| ## Abstract |
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| Modern deepfake detection models have achieved strong performance even on the challenging cross-dataset task. However, detection performance under non-ideal conditions remains very unstable, limiting success on some benchmark datasets and making it easy to circumvent detection. Inspired by the move to a more real-world degradation model in the area of image super-resolution, we have developed a Practical Manipulation Model (PMM) that covers a larger set of possible forgeries. We extend the space of pseudo-fakes by using Poisson blending, more diverse masks, generator artifacts, and distractors. Additionally, we improve the detectors' generality and robustness by adding strong degradations to the training images. We demonstrate that these changes not only significantly enhance the model's robustness to common image degradations but also improve performance on standard benchmark datasets. Specifically, we show clear increases of 3.51% and 6.21% AUC on the DFDC and DFDCP datasets, respectively, over the s-o-t-a LAA backbone. Furthermore, we highlight the lack of robustness in previous detectors and our improvements in this regard. |
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| ## Model Details |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| - **Developed by:** Benedikt Hopf and Radu Timofte |
| - **License:** See github repository: https://github.com/benedikthopf/PMM |
| - **Finetuned from model [optional]:** LAA-Net: https://github.com/10Ring/LAA-Net |
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| ### Model Sources |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** https://github.com/benedikthopf/PMM |
| - **Paper:** [Practical Manipulation Model for Robust Deepfake Detection](https://openaccess.thecvf.com/content/ICCV2025W/AIM/papers/Hopf_Practical_Manipulation_Model_for_Robust_Deepfake_Detection_ICCVW_2025_paper.pdf) |
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| ## Uses |
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| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
| This model is a research project, indended to improve the robustness of deepfake detectors. |
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| ### Direct Use |
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| <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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| The model has been trained and tested on/for the FaceForensics++ benchmark with cross dataset testing on CDFv2, DFDC and DFDCP. |
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| ### Downstream Use |
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| <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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| Detection of newer face-swapping methods. |
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| ### Out-of-Scope Use |
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| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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| The model is not built to detect fully synthetic images. |
| Furthermore, IT IS NOT PERFECT AND MAY ALWAYS MAKE MISTAKES! |
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| ## Bias, Risks, and Limitations |
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| <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| It is only designed and trained for detecting face-swaps/reenactment, not fully synthetic images. |
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| We know that it performs slightly worse on clean data compared to the base model. |
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| ## Citation (Bibtex) |
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| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| ```bibtex |
| @InProceedings{hopf2025practicalmanipulationmodelrobust, |
| author = {Hopf, Benedikt and Timofte, Radu}, |
| title = {Practical Manipulation Model for Robust Deepfake Detection}, |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, |
| month = {October}, |
| year = {2025}, |
| pages = {5675-5684} |
| } |
| ``` |
