liuxuannan/Stochastic-Gradient-Aggregation
Official implementation of the ICCV2023 paper: Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation
This project helps machine learning engineers and researchers create Universal Adversarial Perturbations (UAPs) that are highly effective across many images and different AI models. It takes an existing image classification dataset and a trained AI model, then generates a small, imperceptible distortion (UAP). This UAP can reliably trick a target AI model into misclassifying most images it processes, making the model vulnerable to attack.
No commits in the last 6 months.
Use this if you need to generate highly generalizable Universal Adversarial Perturbations (UAPs) to test the robustness and security of your image classification AI models against sophisticated attacks.
Not ideal if you are looking to create instance-specific adversarial examples, or if your primary goal is to improve the base accuracy of your AI model rather than evaluating its vulnerability.
Stars
28
Forks
3
Language
Python
License
—
Category
Last pushed
Aug 17, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/liuxuannan/Stochastic-Gradient-Aggregation"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.