This repository contains the complete dataset, augmentation assets, and real-world evaluation benchmarks used to train the Champion 3-Class Watermark Classifier (wm_3class_v18_scratch.pt).

The dataset addresses a critical problem in media ingestion: automatically excluding produced media, broadcast stills, and stock photography without falsely excluding authentic personal photographs (0.00% false alarms on personal photos and challenging hard negatives).

1. Dataset Overview

Round-18 training set: 65,733 images in 21,911 aligned triplets (train 56,280 images, validation 9,453). The images themselves are not stored here; section 3 shows how to generate an equivalent set.
Classes (3-way):
1. 0: clean — Authentic personal photographs, public events, street scenes, and challenging hard negatives (foliage, glass reflections, barcodes).
2. 1: publisher — Publisher watermarks, TV channel bugs (BBC News, PBS NewsHour, Fox News), stock photography previews (Dreamstime, Getty, Shutterstock), and agency credits.
3. 2: camera — Authentic smartphone camera timestamps and model watermarks (Huawei, Oppo, Samsung, Xiaomi, Vivo, Tecno, etc.).
Aligned Triplet Structure: Every sample belongs to an aligned triplet sharing the exact same base canvas image, ensuring the classifier isolates the watermark signal rather than background scene semantics.

2. Directory Structure

.
├── README.md                      # Hugging Face Dataset Card
├── augmentation/                  # Raw assets used to synthesize the dataset
│   ├── marks/                     # 2,796 transparent PNG overlay marks
│   │   └── manifest.json          # Metadata for all vector SVGs and procedural marks
│   ├── bases/                     # 6,528 clean base photographs; every triplet is generated from these
│   ├── bases_manifest.json        # Base image mappings and origin categories
│   ├── bases_v21/                 # 321 more clean bases that round 21 trained on (see below)
│   ├── bases_v21_manifest.json    # Their mappings, round-21 fold, and Wikimedia source and title
│   └── scripts/                   # generate_pairs.py builds the triplets, mark_boxes.py their mark boxes
├── benchmarks/                    # Real-world evaluation benchmarks
│   ├── news_broadcast_stills/     # 139 authentic TV news broadcast frames
│   ├── hard_negatives/            # 880 Wikimedia challenging photos (glass, foliage, barcodes)
│   ├── camera_watermarks/         # 131 harvested handset camera watermark test photos
│   └── glm_verified_testset/      # 259 GLM-verified gold standard benchmark images & labels
└── upload_to_hf.py                # 1-click script to upload to Hugging Face Hub

3. Generating the Triplets

The triplets are not stored. Only the clean base photographs and the marks are, and generate_pairs.py builds the clean, publisher and camera images from them. It is deterministic, so every run with the same arguments writes byte-identical images. It needs Pillow, NumPy and the DejaVuSans font.

huggingface-cli download langminer/watermark-benchmark-v18 --repo-type dataset --local-dir watermark-benchmark-v18
cd watermark-benchmark-v18/augmentation/scripts
python generate_pairs.py --out ../../pairs      # pairs/{train,val}/{clean,wm,cam}/<id>.jpg + pairs/manifest.jsonl
python mark_boxes.py ../../pairs                # pairs/boxes.json: the box of the mark in every wm and cam image

manifest.jsonl has one row per image with its label (clean, wm = publisher, cam = camera), fold, base photograph, JPEG quality, and for marked images the mark file, style, kind, tint and opacity.

With the defaults it writes 65,718 images in 21,906 triplets (train 18,706, val 3,200; 94 bases dropped because no mark stayed visible on them), about 3.5 GB. It took about 3 minutes on 16 cores. These are not the exact images the round-18 model was trained on, because the round-18 run drew from a larger pool and an earlier version of composite.py, but the design and class balance are the same.

Round-21 clean bases

Round 21 (wm_3class_v21_scratch) drew its triplets from 1,353 bases. 1,032 of them are already in bases/. The other 321 are in bases_v21/:

bases_v21/verified_clean_bases_v19/ holds 320 Wikimedia Commons photographs chosen as hard negatives: fireworks, concerts, high-visibility vests, sports, bottles, receipts, screens, museum pieces, geometric patterns and gatherings. Each one was checked for a mark of its own before it was admitted. bases_v21_manifest.json gives each photo's Commons title and source URL.
bases_v21/real_photo/real_lectern_speech/v10_302.jpg is one more press photograph.

They are kept out of bases_manifest.json on purpose, so generate_pairs.py still draws only the round-18 pool and the numbers above still hold. The published generator is the round-18 version, so these bases are here as data. They do not make round 21 reproducible on their own.

4. Benchmark Results on Champion Model (`v18`)

Benchmark Set	Round 16 (`v16`)	Round 17 (`v17`)	Round 18 (`v18`)
Dev Selection Metric ($F_{0.5}$)	$0.9723$	$0.9705$	`0.9742`
Dev Precision / Recall	$97.1% / 93.5%$	$97.6% / 94.7%$	`98.2% / 94.5%`
GLM-Verified Test MCC (259 images)	$0.9241$	$0.9380$	`0.9541`
GLM-Verified Test AUC	$0.9972$	$0.9972$	`0.9951`
News Broadcast Bugs Recall	$34.4%$	$49.0%$	`72.9%` (78.1% FP32)
Real Personal Album FA (6,365 photos)	$0.00%$	$0.00%$	`0.00%` @ Cut +5.0
Challenging Hard Negatives (880 photos)	$0.00%$	$0.00%$	`0.00%` @ Cut +5.0

5. License & Citation

The synthetic dataset, annotations, and marks are released under the Apache 2.0 License.
Base photographs originate from Creative Commons sources (Wikimedia, Flickr CC-BY, and public domain collections).

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Multi-Class Watermark & Camera Stamp Dataset (Round 18 Champion)