Mengieong/SEED_balanced

SEED_balanced

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SEED_balanced is the public balanced release of SEED, a benchmark for provenance tracing in sequential deepfake facial edits. Unlike conventional deepfake datasets that focus on single-step manipulations or binary real/fake detection, SEED models multi-step diffusion-based facial editing trajectories and supports three complementary tasks: Authenticity Analysis, Editing Trace Analysis, and Spatial Evidence Analysis. The full SEED benchmark contains 91,526 images with step-wise provenance annotations, while the balanced benchmark partition contains 100,000 images with equal proportions of sequence lengths \(L=0,1,2,3,4\).

Overview

SEED is built from FFHQ and CelebAMask-HQ and edited using diffusion-based pipelines. Each manipulated sample is generated by applying one to four attribute edits sequentially, and each step is logged with provenance metadata including edited attribute, prompt, mask, and editing model.

Supported Tasks

These three tasks are explicitly described in the paper and illustrated in the benchmark overview figure.

Data Construction

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SEED is constructed in three stages:

The editing pipeline uses multiple diffusion editors, including LEdits, SDXL, and SD3-style models fine-tuned with UltraEdit. Prompt templates are varied to preserve edit intent while increasing linguistic diversity.

Prompt Template Examples

These prompt templates are taken from the paper’s dataset construction section.

Dataset Statistics

These distributions are reported in the dataset statistics section of the paper.

Balanced Partition and Split Protocol

Benchmark Evaluation

Official evaluation is conducted on CodaBench using three metrics:

The paper emphasizes that Full-Acc is the strictest metric because the whole predicted edit history must match the ground truth.

Average Results Reported in the Paper

The paper reports that performance drops as edit chains become longer, and that DWT-based FAITH is the strongest average variant overall.

Robustness Settings

The paper also evaluates robustness under:

Repository Contents

This Hugging Face repository hosts the public release only.

This repository does not contain:

• —hidden test labels
• —hidden reference annotations
• —official private evaluation data

Those components are handled through CodaBench.

Intended Usage

This dataset is intended for:

• —deepfake forensics research
• —diffusion-edit provenance tracing
• —edit-order prediction
• —localization and evidence analysis
• —robustness benchmarking under image degradation

Recommended workflow:

1. 1.Download and extract the public training data from this repository.
2. 2.Train or fine-tune your method locally.
3. 3.Validate locally using your own protocol.
4. 4.Submit predictions to CodaBench for official hidden-set evaluation.

CodaBench: [here](https://www.codabench.org/competitions/edit/15351/)

Data Usage Policy

Please use this dataset for research, benchmarking, and forensic analysis only.

Please do not use it for:

• —identity recognition or surveillance
• —face-based profiling
• —deceptive content generation
• —unauthorized inference about real individuals

Users should also respect the licenses and usage conditions of the original source datasets and any benchmark-specific release conditions.

FAITH Baseline Setup and Usage

This repository provides the FAITH baseline and the associated training data package for the SeqDeepFake setting.

Repository Contents

After downloading the full repository, the top-level structure is expected to look like this:

.
├── FAITH/
├── assets/
├── .gitattributes
├── README.md
├── req.txt
└── seqdeepfake_train_data.zip

1. Environment Setup

Create a new conda environment from req.txt, then activate it:

conda create -n <environment-name> --file req.txt
conda activate <environment-name>

Example:

conda create -n faith --file req.txt
conda activate faith

2. Download the Complete Repository

Please download the complete repository contents from Hugging Face, not only the code folder.

You should have all of the following at the repository root:

• —FAITH/
• —assets/
• —req.txt
• —README.md
• —seqdeepfake_train_data.zip

If you download from the web interface instead, make sure the downloaded archive is fully extracted before continuing.

3. Prepare the Training Data

The training data is provided as:

seqdeepfake_train_data.zip

Unzip this file into the FAITH directory, then rename the extracted folder to data.

Run the following commands from the repository root:

unzip seqdeepfake_train_data.zip -d FAITH/

Then rename the extracted folder to data.

For example, if the extracted folder is named seqdeepfake_train_data, run:

mv FAITH/seqdeepfake_train_data FAITH/data

After this step, the expected structure should be:

.
├── FAITH/
│   ├── data/
│   ├── ...
├── assets/
├── .gitattributes
├── README.md
├── req.txt
└── seqdeepfake_train_data.zip

4. Verify the Data Placement

Before running the baseline, confirm that the dataset is located at:

FAITH/data

That is the expected folder name used by the baseline instructions in this repository.

5. Run the Baseline

After the environment is ready and the dataset has been placed in FAITH/data, enter the FAITH directory and run the baseline script.

cd FAITH
python train.sh

Please replace <your_main_script>.py with the actual entry script used in your repository.

If your repository provides separate scripts for training and evaluation, use the appropriate one instead, for example:

cd FAITH
python train.sh

or

cd FAITH
python test.sh

Notes

1. 1.Make sure you download the full repository contents, not only individual files.
2. 2.Make sure the extracted dataset folder is renamed exactly to data.
3. 3.If unzip is not installed on your system, install it first or extract the archive manually.
4. 4.If the extracted folder name is different on your machine, rename that extracted folder to FAITH/data.
5. 5.If the project has a custom launch script, use that script instead of the generic python <your_main_script>.py command.

Troubleshooting

PackagesNotFoundError during conda creation

This usually means some packages in req.txt are unavailable in your current conda channels. In that case, try updating conda first, or recreate the environment with the channels required by your project.

The dataset cannot be found

Check that the final path is exactly:

FAITH/data

python: can't open file ...

This means the entry script name is different from the placeholder command in this README. Please replace <your_main_script>.py with the actual script name in the FAITH folder.

If you are preparing the Hugging Face repository page, you can copy this file directly as the project README.md and then replace the script placeholder with the exact training or evaluation command used by your codebase.