About This Tool

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

Primary Use Case

AdvBox is used by AI security researchers and practitioners to generate adversarial examples that fool neural networks, detect adversarial inputs in large datasets, and benchmark the robustness of machine learning models. It supports multiple deep learning frameworks and provides zero-coding command line tools, making it accessible for both developers and security analysts aiming to test and harden AI systems against adversarial attacks.

Key Features

Generation of adversarial examples for PaddlePaddle, PyTorch, Caffe2, MxNet, Keras, and TensorFlow
Benchmarking robustness of machine learning models
Zero-coding command line tool for easy adversarial example generation
AdvDetect module for detecting adversarial examples in large datasets
AdvPoison module for data poisoning attacks
AdvSDK: Lightweight SDK for PaddlePaddle adversarial example generation
Support for AI security applications like face recognition attacks and fake face detection
Open-source demonstrations including stealth T-shirt for evading smart cameras

Insights & Recommendations

AdvBox is inspired by and based on FoolBox v1, supporting a broad range of AI frameworks; users should ensure compatibility with their specific model frameworks. It is recommended to review individual module READMEs for detailed usage and dependencies. The tool is primarily designed for research and security testing purposes and should be used responsibly.

Installation

Ensure Python 3.x is installed
Clone the repository: git clone https://github.com/advboxes/AdvBox.git
Navigate to the cloned directory: cd AdvBox
Install required dependencies (typically via pip install -r requirements.txt)
Refer to specific module README files for additional setup instructions (e.g., advsdk/README.md)

Usage

advbox --help

Displays help information and available commands for the AdvBox command line tool

advbox generate -m <model> -i <input_image> -o <output_path>

Generates adversarial examples for a specified model using an input image and saves output

python advbox_family/AdvDetect/detect.py -d <dataset_path>

Runs adversarial example detection on a large dataset

python applications/fake_face_detect/api.py

Starts the RESTful API service for detecting fake faces in images or videos

Smart Usage Notes

Integrate AdvBox into AI model development pipelines to proactively identify adversarial vulnerabilities before deployment.Use AdvDetect module to enhance anomaly detection systems for adversarial input identification in production environments.Leverage AdvPoison for red team exercises simulating data poisoning attacks to test data integrity controls.Combine AdvBox with threat intelligence feeds to automate adversarial attack pattern updates for continuous defense tuning.Employ AdvSDK for custom adversarial example generation tailored to specific AI models to improve robustness testing.

OpenSec Atlas

AdvBox