Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,122 @@
+---
+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.