Update README.md

README.md

@@ -6,7 +6,192 @@ language:
 - en
 tags:
 - privacy
+- cv
 pretty_name: CPRT Dataset
 size_categories:
 - 1K<n<10K
----
+---
+# Dataset Card for CPRT-Bench
+
+<!-- Provide a quick summary of the dataset. -->
+
+CPRT-Bench is a benchmark dataset for assessing privacy risk in images, designed to model privacy as a graded and composition-dependent phenomenon.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+The dataset contains approximately 6.7K images annotated with:
+- Ordinal severity levels (4 levels of privacy risk)
+- Continuous risk scores (fine-grained privacy assessment)
+
+All images are sourced from the VISPR (Visual Privacy Dataset). CPRT-Bench augments these images with structured annotations for privacy risk evaluation.
+
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Paper:** [https://arxiv.org/pdf/2603.21573]
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+CPRT-Bench is intended for:
+- Evaluating privacy risk prediction in computer vision systems
+- Benchmarking multimodal models on privacy perception tasks
+- Studying calibration and ranking in risk prediction
+- Research on context-aware and compositional reasoning in vision models
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+his dataset is not suitable for:
+- Real-world privacy decision-making systems without additional safeguards
+- Legal or regulatory enforcement
+- Applications requiring culturally universal definitions of privacy
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+Each example includes:
+
+- **`id`**: Filename ID corresponding to a VISPR image
+- **`binary_labels`**: A nested dictionary of binary attributes grouped by privacy level
+- **`level`**: An integer severity label from 1 to 4
+- **`score`**: A floating-point privacy-risk score
+
+The `binary_labels` field is organized hierarchically:
+
+- `level1`: attributes that uniquely and directly identify a specific individual on their own
+- `level2`: attributes that can reference a person or reveal sensitive personal information
+- `level3`: attributes that are non-sensitive and non-identifying in isolation, but can contribute to identity linkage or profiling when combined with other non-uniquely identifying information
+- `level4`: attributes that are generally benign and non-identifying, but may be regarded as private information depending on the context
+
+Example structure:
+
+```json
+{
+  "level1": {
+    "biometrics": 0/1,
+    "gov_ids": 0/1,
+    "unique_body_markings": 0/1
+  },
+  "level2": {
+    "contact_details": 0/1,
+    "full_legal_name": 0/1,
+    "non_unique_id": 0/1,
+    "medical_data": 0/1,
+    "financial_data": 0/1,
+    "beliefs": 0/1,
+    "nudity": 0/1,
+    "disability": 0/1,
+    "emotion_mental_health": 0/1,
+    "race_ethnicity": 0/1
+  },
+  "level3": {
+    "age": 0/1,
+    "gender": 0/1,
+    "location": 0/1,
+    "activities": 0/1,
+    "lifestyle": 0/1
+  },
+  "level4": {
+    "property_assets": 0/1,
+    "documents": 0/1,
+    "metadata": 0/1,
+    "background_people": 0/1
+  }
+}
+```
+
+### Loading Instructions
+
+CPRT-Bench contains annotation data only and does not distribute the underlying VISPR images. Users must download the VISPR dataset separately and resolve each id field to the corresponding image file.
+The dataset adopts the VISPR split protocol:
+	- The training split is derived from the VISPR validation split
+    - The test split is derived from the VISPR test split
+
+1. Download VISPR dataset:
+   - VISPR-test [link](https://datasets.d2.mpi-inf.mpg.de/orekondy17iccv/test2017.tar.gz)
+   - -VISPR-val [link](https://datasets.d2.mpi-inf.mpg.de/orekondy17iccv/val2017.tar.gz) 
+
+2. Load dataset:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("timtsapras23/CPRT-Bench")
+```
+
+A simple way to load the image for each example is to search for the file that matches the VISPR `id`:
+
+```python
+import os
+from glob import glob
+from PIL import Image
+
+
+VISPR_ROOT = "/path/to/vispr/images"
+
+def load_vispr_image(example):
+    image_id = example["id"]
+
+    candidates = [
+        os.path.join(VISPR_ROOT, f"{image_id}.jpg"),
+        os.path.join(VISPR_ROOT, f"{image_id}.png"),
+        os.path.join(VISPR_ROOT, image_id),
+    ]
+
+    image_path = next((p for p in candidates if os.path.exists(p)), None)
+    if image_path is None:
+        matches = glob(os.path.join(VISPR_ROOT, f"{image_id}.*"))
+        if matches:
+            image_path = matches[0]
+        else:
+            raise FileNotFoundError(f"Could not find an image for id={image_id}")
+
+    example["image"] = Image.open(image_path).convert("RGB")
+    return example
+
+# Example: load the first split with images attached
+# dataset["train"] = dataset["train"].map(load_vispr_image)
+```
+## Leaderboard
+
+
+| Model | Spearman ρ ↑ | Pearson r ↑ | MAE ↓ |
+|------|--------------|-------------|-------|
+| **Gemini 3 Flash** | **0.872** | **0.884** | **0.140** |
+| GPT-5.2 | 0.844 | 0.850 | 0.158 |
+| Qwen3-VL (8B) + SFT (80 steps) | 0.762 | 0.799 | **0.140** |
+| Qwen3-VL (4B) + SFT (80 steps) | 0.753 | 0.790 | 0.142 |
+| Llama 4 Maverick | 0.763 | 0.728 | 0.233 |
+| Qwen3-VL (32B) | 0.753 | 0.726 | 0.224 |
+| Qwen3-VL (8B) | 0.751 | 0.636 | 0.291 |
+| Pixtral (12B) | 0.720 | 0.616 | 0.311 |
+| MiniCPM-V (8B) | 0.610 | 0.616 | 0.237 |
+| Llama 3.2 VL (11B) | 0.571 | 0.460 | 0.344 |
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+```bibtex
+@article{tsaprazlis2026cprt,
+  title={Rethinking Visual Privacy: A Compositional Privacy Risk Framework for Severity Assessment with VLMs},
+  author={Tsaprazlis, Efthymios and others},
+  journal={arXiv preprint arXiv:2603.21573},
+  year={2026}
+}
+```