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https://github.com/ethanm88/GPTGeoChat

Repository for "Granular Privacy Control for Geolocation with Vision Language Models"
https://github.com/ethanm88/GPTGeoChat

Last synced: about 2 months ago
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Repository for "Granular Privacy Control for Geolocation with Vision Language Models"

Host: GitHub
URL: https://github.com/ethanm88/GPTGeoChat
Owner: ethanm88
Created: 2024-06-15T13:36:03.000Z (7 months ago)
Default Branch: main
Last Pushed: 2024-11-18T15:18:14.000Z (2 months ago)
Last Synced: 2024-11-18T16:57:35.839Z (2 months ago)
Language: Python
Homepage:
Size: 2.71 MB
Stars: 6
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# GPTGeoChat🌎: Benchmarking Conversational Geolocation
Repository for [Granular Privacy Control for Geolocation with Vision Language Models](https://arxiv.org/abs/2407.04952)

## Main Datasets 🗎
### Downloads and Directory Structure ⬇️
The full human annotated GPTGeoChat and AI-generated GPTGeoChat_Synthetic are available for download at the following links:
* [GPTGeoChat (1.43 GB)](https://www.mediafire.com/file/luwlv2p9ofgxdb5/human.zip/file)
* [GPTGeoChat_Synthetic (227 MB)](https://www.mediafire.com/file/chvqvde6xm7ofqa/synthetic.zip/file)

The directory structure of GPTGeoChat:
```
human
│
└───test
│ │
│ └───annotations
│ │ ...
│ └───images
│ ...
│
└───train
│ │
│ └───annotations
│ │ ...
│ └───images
│ ...
│
└───val
│
└───annotations
│ ...
└───images
...
```
While the structure for GPTGeoChat_Synthetic does not include a train/test/val split:
```
synthetic
│
└───annotations
│ ...
└───images
...
```
These datasets include both images and the associated conversations. Images are in files named `images/{id}.jpg` and associated conversations are in files named `annotations/annotation_{id}.json`.

### Annotated Dialogue Structure 👨‍💻↔️🤖
Annotated dialogues in GPTGeoChat and GPTGeoChat_Synthetic are structured as follows:
```json
{
"image_path": "../images/{id}.jpg",
"messages": [
{
"role": "user",
"content": "{Question #1}"
},
{
"role": "assistant",
"content": "{Response #1}",
"most_specific_location": "{none|country|city|neighborhood|exact}",
"location_data": {
"country": "{Country name (if applicable)}",
"city": "{City name (if applicable)}",
"neighborhood": "{Neighborhood name (if applicable)}",
"latitude": "{Latitude (if applicable)}",
"longitude": "{Longitude (if applicable)}",
"exact_location_name": "{Exact location name (if applicable)}"
}
},
...
]
}
```
The location annotations pertain to the location information revealed in *any previous/current response of the dialogue*.

`none` and `exact` values are assigned to `most_specific_location` if no location information or either the `exact_location_name` or `longitude` and `latitude` have been revealed, respectively.

## Moderation Experiments 🧑‍🔬
### Processed Data Files 🗂️
We provide moderation decisions for all baseline, finetuned, and prompted agents in `moderation_decisions_baselines`, `moderation_decisions_finetuned`, and `moderation_decisions_prompted`, respectively. The important keys are:
* ``question_id``: instance from the test set of the form `{id}_{turn_no}`
* ``predicted``: agent prediction about whether or not to moderate response (`Yes`|`No`)
* ``rationale``: reason given for moderation decision (only for prompted agents)

### Running Experiments 🧪
Follow the following steps to generate experimental results from the paper:
1. Clone the repository:
```bash
git clone https://github.com/ethanm88/GPTGeoChat.git && cd GPTGeoChat
```
2. Download [GPTGeoChat](https://www.mediafire.com/file/luwlv2p9ofgxdb5/human.zip/file) in the ``GPTGeoChat`` directory.

3. Unzip GPTGeoChat:
```bash
mkdir gptgeochat && unzip human.zip && rm human.zip
```
4. Generate Ground Truth Files. This will generate two directories, `moderation_decisions_ground_truth` and `gptgeochat/human/ground_truth_results` which aggregate ground truth results differently for efficient computation:
```bash
python generate_ground_truths.py
```
5. Run Experiments
```bash
python generate_eval_metrics.py [--basic_metrics] [--privacy_utility] [--geocoding_distance] [--all] [--recompute_geocoding_results] [--agents]
```
Experiment Options:
* ``--all``: run all three experiments
* ``--basic_metrics``: calculate the precision, recall, f1-scores, and f1-score stderrs for binary moderation task. This data was used to generate Figure 3.
* ``--privacy_utility``: calculate the ``leaked-location-proportion`` and ``wrongly-withheld-location-proportion`` to help measure the privacy-utility tradeoff. This data was used to generate Figure 4.
* ``--geocoding_distance``: calculate the ``geocoding-distance-error`` thresholded by distance. This data was used to generate Figure 5. \
**Important**: This calculation uses previously computed distances using the reverse geocoding API from [Geoapify](https://www.geoapify.com/reverse-geocoding-api/). These files are saved under ``api_distance_responses``.
* ``--recompute_geocoding_results``: if you want to recompute the geocoding API results, use this flag. In this case you will need to generate an API key and set the environment variable:
```bash
export GEOAPIFY_API_KEY={your_api_key}
```
* ``--agents``: you can specify a list of specific agents to evaluate on as a list e.g. ``--agents GPT4V synthetic_num_examples=1000``

## Benchmark Your Agents 🚀
Benchmarking custom agents is easy! Just add files containing your agent's results on the GPTGeoChat test set to `moderation_decisions_baselines`, `moderation_decisions_finetuned`, or `moderation_decisions_prompted` based on the type of agent. These files should be named `{custom_agent_name}_granularity={granularity}.jsonl`. Running `generate_eval_metrics.py` with the correct arguments will then evaluate your agents. Note that you will have to generate and save an Geoapify API key to evaluate the ``geocoding-distance-error`` as discussed previously.

## Citation ✍️
```
@inproceedings{mendes-etal-2024-granular,
title = "Granular Privacy Control for Geolocation with Vision Language Models",
author = "Mendes, Ethan and
Chen, Yang and
Hays, James and
Das, Sauvik and
Xu, Wei and
Ritter, Alan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.957",
pages = "17240--17292",
abstract = "Vision Language Models (VLMs) are rapidly advancing in their capability to answer information-seeking questions. As these models are widely deployed in consumer applications, they could lead to new privacy risks due to emergent abilities to identify people in photos, geolocate images, etc. As we demonstrate, somewhat surprisingly, current open-source and proprietary VLMs are very capable image geolocators, making widespread geolocation with VLMs an immediate privacy risk, rather than merely a theoretical future concern. As a first step to address this challenge, we develop a new benchmark, GPTGeoChat, to test the capability of VLMs to moderate geolocation dialogues with users. We collect a set of 1,000 image geolocation conversations between in-house annotators and GPT-4v, which are annotated with the granularity of location information revealed at each turn. Using this new dataset we evaluate the ability of various VLMs to moderate GPT-4v geolocation conversations by determining when too much location information has been revealed. We find that custom fine-tuned models perform on par with prompted API-based models when identifying leaked location information at the country or city level, however fine-tuning on supervised data appears to be needed to accurately moderate finer granularities, such as the name of a restaurant or building.",
}

```
## Acknowledgement
We thank Azure’s Accelerate Foundation Models Research Program for graciously providing access to API-based ``GPT-4o``.
This research is supported in part by the NSF (IIS-2052498, IIS-2144493 and IIS-2112633), ODNI, and IARPA via the HIATUS program (2022-22072200004). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of NSF, ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein.