adversarial-perturbation-finder

# Adversarial Perturbation Finder

## Purpose
Identify and analyze adversarial perturbations in data (e.g., images, text, or audio) designed to deceive AI models in a CTF or educational context.

## Steps
1. **Data Inspection**: Analyze input data for subtle, non-human-perceptible changes (e.g., pixel-level noise, odd character sequences, or hidden frequencies).
2. **Model Sensitivity Mapping**: Identify how specific perturbations affect the model's confidence or classification (e.g., does a specific noise pattern trigger an incorrect label?).
3. **Exploit Generation**: Create and verify adversarial examples meant to trigger target behaviors in the model.

## Output
- Analysis of adversarial triggers.
- Logic-based exploit generation.
- Structured reasoning for adversarial inputs.