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.
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.
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.