| license: apache-2.0 | |
| pipeline_tag: object-detection | |
| library_name: transformers | |
| # Adversarial Attention Perturbations for Large Object Detection Transformers (AFOG) | |
| This repository hosts the implementation for the paper [Adversarial Attention Perturbations for Large Object Detection Transformers](https://huggingface.co/papers/2508.02987). | |
| **AFOG** (Attention-Focused Offensive Gradient) is a novel adversarial perturbation method designed to attack both large transformer-based object detectors and conventional CNN-based detectors with a unified adversarial attention framework. It leverages a learnable attention mechanism to focus perturbations on vulnerable image regions, significantly increasing attack performance while maintaining visual imperceptibility. | |
| <div align="center"> | |
| <img src="https://github.com/IDEA-Research/AFOG/raw/main/assets/examples.png" width="100%" height="100%"/> | |
| </div> | |
| ## Key Features | |
| * **Unified Attack Framework**: Effective against both transformer-based and CNN-based object detectors. | |
| * **Attention-Focused Perturbations**: Utilizes a learnable attention mechanism to target vulnerable image regions, improving attack efficacy. | |
| * **Efficient and Stealthy**: Generates strategically designed, visually imperceptible perturbations that cause well-trained models to fail. | |
| ## Installation and Usage | |
| For detailed installation instructions, environment setup, and comprehensive usage examples (including Jupyter notebooks for single-image attacks and Slurm scripts for COCO evaluation), please refer to the official [AFOG GitHub repository](https://github.com/IDEA-Research/detrex). |