appr-photos

Adversary-Adaptive Representation Learning for Privacy-Preserving Image Tasks

CS 750/850 Course Project — University of New Hampshire

Overview

This project learns image representations that preserve utility labels (for example, emotion or category) while suppressing sensitive biometric information (identity, gender, age).

Architecture

Image → CNN Feature Extractor
      → Privacy Filter (Conv1D + VIB)
      → Task Model (Attention Pooling + Classifier)  [utility]
      → GRL → Multi-Head Adversary                   [privacy]

Training objective: min_{θ,φ} max_{ψ} L_utility - λ · L_privacy

NVIDIA GPU Quick Start

bash scripts/setup_nvidia_env.sh appr-photos-cuda 3.10 cu124
conda activate appr-photos-cuda
nvidia-smi

If your driver requires a different wheel, use cu121 or another CUDA tag:

bash scripts/setup_nvidia_env.sh appr-photos-cuda 3.10 cu121

Dataset Download

Recommended practical dataset: CelebA.

This repo downloads CelebA through torchvision's official dataset integration and builds a repo-compatible metadata.csv automatically. The default setup uses:

• utility label: Smiling vs not_smiling
• privacy labels: identity and gender

bash scripts/download_data.sh celeba data/raw/celeba

This writes the prepared dataset to data/raw/celeba and creates metadata.csv.

Custom Dataset Preparation

Organize images under data/raw/celeba:

data/raw/celeba/
  <class_name>/
    <speaker_id>/image_001.jpg
    <speaker_id>/image_002.jpg

Or place metadata.csv in data/raw/celeba/ with fields such as: filename,utility_label,speaker_id,gender,age.

Verification and metadata generation:

python scripts/prepare_datasets.py --verify --root data/raw/celeba
python scripts/prepare_datasets.py --build-metadata --root data/raw/celeba
python scripts/prepare_datasets.py --stats --root data/raw/celeba

Training

# CelebA NVIDIA-optimized config
python scripts/train.py --config configs/experiment/celeba_nvidia.yaml

# CelebA baseline training
python scripts/train.py --config configs/experiment/celeba_baseline.yaml

# Precompute features for faster training loops (optional)
python scripts/precompute_features.py --config configs/experiment/celeba_baseline.yaml
python scripts/train.py --config configs/experiment/celeba_cached.yaml

# Override config values from CLI
python scripts/train.py --config configs/experiment/celeba_baseline.yaml training.num_epochs=20 training.lambda_privacy=0.05

The CelebA configs use smiling classification as the utility task and identity/gender as the privacy targets.

CPU / Non-NVIDIA Setup

conda create -n appr-photos-cpu -y python=3.10 pip
conda activate appr-photos-cpu
pip install torch torchvision
pip install -e .

Evaluation

python scripts/evaluate.py --checkpoint outputs/celeba_nvidia/checkpoints/best_model.pt

Lambda Sweep (Pareto Frontier)

python scripts/sweep_lambda.py --config configs/experiment/celeba_baseline.yaml --epochs 20

Visualization

python scripts/visualize.py --checkpoint outputs/celeba_nvidia/checkpoints/best_model.pt

For report-ready figures from a trained run:

python scripts/generate_report_figures.py \
  --checkpoint outputs/celeba_nvidia/checkpoints/best_model.pt \
  --output_dir outputs/report_figures

Tests

pytest tests/ -v

Evaluation Metrics

Utility (higher is better): UAR, Weighted Accuracy, Macro F1
Privacy (lower is more private): identity accuracy, gender accuracy, de-identification rate, MI(Z; S)

Project Structure

src/aapr/
├── data/          # Photo dataset loaders and split/collation utils
├── features/      # CNN image feature extractor + feature cache
├── models/        # Privacy filter, task model, adversary, GRL
├── training/      # Adversarial trainer, losses, schedulers, metrics
├── evaluation/    # Evaluator, cross-dataset, Pareto analysis
├── visualization/ # Embeddings, training curves, Pareto plots
└── utils/         # Config, logging, seed, device detection

APPR-PHOTOS