README.md

---
license: mit
language:
- en
- ar
tags:
- multimodal
- misinformation
- forgery-detection
- fnd-clip
- dct
size_categories:
- 10K<n<100K
---

# MultiGuard Phase 2 — Multimodal Misinformation Detection Dataset & Caches

This dataset packages everything needed to reproduce **Phase 2** of the
[MultiGuard](https://github.com/Rashidbm/Multimodal-fake-news-detection)
project without redoing the heavy preprocessing.

## What's inside

| File | Size | Contents |
|---|---|---|
| `forensic_3class.csv` | 3.7 MB | 18,000-sample 3-class dataset (Real / Manipulated / OOC), balanced 6,000 per class, deterministic 70/15/15 per-class split (seed 42). |
| `dct_cache.tar.gz` | 2.9 GB | Precomputed DCT maps for all 18,000 images. Each entry is a `[1, 224, 224]` float32 tensor (BGR→YCbCr→Y, 2D DCT, log scale, per-image min-max). Filename is md5 of the absolute image path. Extract into `data/processed/dct_cache/`. |
| `fnd_features_clean.tar.gz` | 37 MB | FND-CLIP `v_semantic` features (512-dim) computed with **leak-free FND-CLIP** — pretrained ResNet50 / BERT / CLIP encoders + random frozen modality-attention head. No task-specific weights. Filename is md5(text \|\| image_path). Extract into `data/processed/fnd_features_clean/`. |
| `fnd_features_leaked.tar.gz` | 37 MB | Same format, but computed using `outputs/v1_ooc/best.pt` as the FND-CLIP checkpoint. **Numbers from training on this cache are inflated by label leakage** (see Known Issues). Provided for completeness only. |
| `step1_forensic_baseline_best.pt` | 46 MB | Step 1 forensic-only baseline checkpoint (ResNet18 on DCT + linear head). Test F1 ~0.47, MMFakeBench transfer ~0.17. |
| `step2_full_pipeline_LEAKED_best.pt` | 59 MB | Step 2 full pipeline checkpoint trained with the leaked v_semantic. **Do not trust the metrics from this** — kept for archival. |

## Class definitions

| Label | Name | Source |
|---|---|---|
| 0 | Real | DGM4 origin (real images, real news from BBC / Guardian / USA Today / Washington Post) |
| 1 | Manipulated | DGM4 face_swap, face_attribute (image-side manipulation, real news source) |
| 2 | OOC | NewsCLIPpings out-of-context pairs (real images, mismatched captions) |

## Splits

| Split | Per class | Total |
|---|---|---|
| train | 4,200 | 12,600 |
| val | 900 | 2,700 |
| test | 900 | 2,700 |

## How to use (with the code repo)

```bash
git clone https://github.com/Rashidbm/Multimodal-fake-news-detection.git
cd Multimodal-fake-news-detection

# Pull the data from this dataset
huggingface-cli download Rashidbm/multiguard-phase2-data \
    --repo-type dataset --local-dir hf_data/

# Place the CSV
mkdir -p data/processed
cp hf_data/forensic_3class.csv data/processed/

# Extract caches
tar -xzf hf_data/dct_cache.tar.gz             -C data/processed/
tar -xzf hf_data/fnd_features_clean.tar.gz    -C data/processed/

# (Optional) drop in the trained checkpoints
mkdir -p outputs/forensic_baseline outputs/full_pipeline
cp hf_data/step1_forensic_baseline_best.pt outputs/forensic_baseline/best.pt
```

The `image_path` column in the CSV uses absolute paths from the original
machine (`/Users/rashid/...`). Either replicate that layout or rewrite
the paths:

```bash
sed -i '' 's|/Users/rashid/multimodaldetection|/your/repo/path|g' \
    data/processed/forensic_3class.csv
```

## Image data NOT included

The raw images are too large and have their own licenses. Get them from:

- DGM4 (origin + manipulation) — `rshaojimmy/DGM4` on HuggingFace
- NewsCLIPpings test split — VisualNews + NewsCLIPpings annotations
- MMFakeBench (val/test for transfer eval) — `liuxuannan/MMFakeBench`

You only need the raw images if you want to retrain the DCT cache from
scratch or use the visual / CLIP streams; the precomputed caches in this
dataset cover the standard Phase-2 training loop.

## Known issues

1. **`fnd_features_leaked.tar.gz` and `step2_full_pipeline_LEAKED_best.pt`**
   were produced using a FND-CLIP checkpoint (`outputs/v1_ooc/best.pt`)
   that was trained on a binary OOC vs not-OOC task. **84% of our test
   OOC samples were in that checkpoint's training set**, so v_semantic
   for those samples literally encodes the OOC label. The Step 2 OOC
   F1 of 0.98 reported in the GitHub HANDOFF.md is mostly this leak.
   Use `fnd_features_clean.tar.gz` for honest numbers (~0.54 F1).

2. **Source-distribution shortcut**: NewsCLIPpings OOC images are
   stored at ~22% lower JPEG quality than DGM4 origin/manipulated images
   (36.7 KB vs 47.5 / 45.8 KB on average). DCT can shortcut on this. To
   eliminate, re-encode all images at uniform JPEG quality before
   computing the DCT cache.

## License

MIT for the splits / metadata. Underlying image and text data are
governed by the licenses of the source datasets (DGM4, NewsCLIPpings,
VisualNews).

## Citation

If you use this in academic work, cite the upstream datasets:

- DGM4: Shao et al., "Detecting and Grounding Multi-Modal Media Manipulation," CVPR 2023.
- NewsCLIPpings: Luo et al., "NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Media," EMNLP 2021.
- MMFakeBench: Liu et al., "MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark," 2024.
- FND-CLIP: Zhou et al., "Multimodal Fake News Detection via CLIP-Guided Learning," ICME 2023.