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Co-authored-by: bui huynh quoc anh <bui huynh quoc anh@users.noreply.huggingface.co>

README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+- other
+tags:
+- 3D-Gaussian-Splatting
+- adversarial
+- benchmark
+- anomaly-detection
+- security
+- computer-vision
+- 3DGS
+- poisoning
+pretty_name: Poison-3DGS
+size_categories:
+- 100G<n<1T
+---
+
+# Poison-3DGS: A Stage-wise Benchmark for Detectability of 3DGS Poisoning Attacks
+
+**Paper:** *Characterizing Detectability in 3DGS Poisoning: A Stage-wise Benchmark* (NeurIPS 2026 Evaluations & Datasets Track)
+
+Poison-3DGS is a benchmark for evaluating detection methods against adversarial poisoning of 3D Gaussian Splatting (3DGS) assets. It covers **4 attack families** spanning the major threat categories — integrity, availability, and steganography — across **37 scenes** and **4 pipeline stages**, providing a unified evaluation protocol for stage-wise detectability analysis.
+
+> **This is a review subset (~127 GB).** The full benchmark (~6 TB including all checkpoints) will be released upon acceptance.
+
+---
+
+## Benchmark Overview
+
+| Property | Value |
+|----------|-------|
+| Clean scenes | 37 |
+| Datasets | Free, Mip-NeRF 360, Tanks-and-Temples |
+| Attack families | 4 |
+| Total adversarial variants (full benchmark) | 414 |
+| Variants in this subset | 94 (anchor configs per attack) |
+| Detection stages covered | 4 |
+| Total size (this subset) | ~127 GB |
+
+---
+
+## Dataset Structure
+
+```
+Poison-3DGS/
+├── clean/                              # Clean reference scenes
+│   └── {free,mip-nerf-360,tanks-and-temples}/<scene>/
+│       ├── images/                     # Raw RGB images (10 scenes only*)
+│       ├── sparse/0/                   # COLMAP reconstruction (10 scenes only*)
+│       └── ply/
+│           ├── iteration_7000/point_cloud.ply    # early checkpoint (24 scenes)
+│           ├── iteration_20000/point_cloud.ply   # mid checkpoint (24 scenes)
+│           └── iteration_30000/point_cloud.ply   # final (all 37 scenes)
+│
+├── stealthattack/                      # Integrity attack (20 scenes)
+│   └── poisoned/{dataset}/<scene>/{difficulty}/{object}_{size}/
+│       ├── config.yaml
+│       ├── mask.png / trigger.png / target.png
+│       ├── sparse/0/points3D.ply       # poisoned SfM (injected cluster)
+│       └── ply/
+│           ├── iteration_7000/point_cloud.ply
+│           ├── iteration_20000/point_cloud.ply
+│           └── iteration_30000/point_cloud.ply
+│
+├── poisonsplat/                        # Availability attack (20 scenes)
+│   └── poisoned/{dataset}/<scene>/constrained/eps16_100/
+│       ├── config.yaml
+│       ├── images/                     # adversarially perturbed training images
+│       └── ply/
+│           ├── iteration_3000/point_cloud.ply
+│           ├── iteration_15000/point_cloud.ply
+│           ├── iteration_21000/point_cloud.ply
+│           └── iteration_30000/point_cloud.ply
+│
+├── gsw/                                # Steganographic watermark — 3D-GSW (37 scenes)
+│   └── poisoned/{dataset}/<scene>/msg64/lambda_mid/
+│       ├── config.yaml
+│       └── ply/iteration_30000/point_cloud.ply
+│
+└── guardsplat/                         # Steganographic watermark — GuardSplat (37 scenes)
+    └── poisoned/{dataset}/<scene>/msg48/lambda_mid/
+        ├── config.yaml
+        └── ply/iteration_30000/point_cloud.ply
+```
+
+*Clean images + COLMAP are included for 10 representative scenes (3 Free + 3 Mip-NeRF 360 + 4 T&T). For the remaining 27 scenes, download from the original datasets (links below).
+
+---
+
+## Quick Start
+
+```python
+from huggingface_hub import snapshot_download
+from plyfile import PlyData
+import numpy as np
+
+# Download full subset (~127 GB) or a specific attack
+path = snapshot_download(
+    repo_id="anonymous-user-submission/Poison-3DGS",
+    repo_type="dataset",
+    # Narrow download to one attack:
+    # allow_patterns=["poisonsplat/**", "clean/**"]
+)
+
+# Load a PoisonSplat final model and compare to clean reference
+ps_ply = PlyData.read(f"{path}/poisonsplat/poisoned/tanks-and-temples/horse/constrained/eps16_100/ply/iteration_30000/point_cloud.ply")
+clean_ply = PlyData.read(f"{path}/clean/tanks-and-temples/horse/ply/iteration_30000/point_cloud.ply")
+
+print(f"Poisoned Gaussians : {len(ps_ply['vertex']):,}")
+print(f"Clean Gaussians    : {len(clean_ply['vertex']):,}")
+print(f"Inflation ratio    : {len(ps_ply['vertex']) / len(clean_ply['vertex']):.1f}x")
+
+# Load a StealthAttack poisoned sparse cloud
+from pathlib import Path
+sa_sparse = PlyData.read(f"{path}/stealthattack/poisoned/mip-nerf-360/bicycle/median/trash_can_large/sparse/0/points3D.ply")
+print(f"Poisoned SfM points: {len(sa_sparse['vertex']):,}")
+```
+
+---
+
+## Evaluation Protocol
+
+- **Task**: Binary scene-level classification (poisoned = 1, clean = 0)
+- **Primary metric**: AUROC · **Secondary**: AUPRC, FPR@95TPR
+- **Train/test split**: `--eval --llffhold 8` (every 8th camera held out, consistent across all variants)
+---
+
+## Responsible AI
+
+**Intended use**: Research on detection and defense methods for adversarial attacks on 3D Gaussian Splatting. Supports development of trustworthy 3DGS systems for security-sensitive applications.
+
+**Out-of-scope use**: This benchmark should not be used to improve offensive attack capabilities in isolation from corresponding defensive research.
+
+**Limitations**:
+- This release is a **subset** (anchor configs only). Full hyperparameter coverage is in the complete benchmark, released upon paper acceptance.
+- Clean images and COLMAP are included for 10 of 37 scenes. The remaining 27 require downloading from original sources.
+- StealthAttack and PoisonSplat checkpoint PLYs cover 3–4 snapshots, not the full 16-checkpoint training trajectory.
+- PoisonSplat scene coverage skews toward Tanks-and-Temples (smaller scenes selected to fit the 300 GB HuggingFace limit).
+
+**Data provenance**: Poisoned variants are derived from publicly available 3D reconstruction datasets (Free, Mip-NeRF 360, Tanks-and-Temples) under their respective non-commercial research licenses.
+
+**Personal/sensitive information**: No personal data. All scenes contain buildings, objects, and outdoor environments with no identifiable individuals.
+
+**Construct validity**: Each attack variant is validated against its success criteria before inclusion. PoisonSplat variants confirm ≥2× Gaussian inflation; 3D-GSW variants confirm ≥0.95 bit accuracy with ≤1.0 dB PSNR drop; GuardSplat variants confirm ≥0.75 bit accuracy with ≤7.0 dB PSNR drop.
+
+**Social impact**: Advances detection of adversarial manipulation in 3D scene representations, supporting reliable deployment of 3DGS in autonomous systems, AR/VR, and 3D content pipelines.
+
+---
+
+## License
+
+This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). The underlying scene images retain the licenses of their original datasets.