Verified:GitHub

ix-adversarial

Evaluate machine learning model robustness against adversarial attacks like FGSM and PGD.

Updated Mar 12, 2026

Search Tags:#robustness testing #poisoning detection #adversarial machine learning #defensive strategies #privacy enhancement

Ready to run

npx skills add https://github.com/GuitarAlchemist/ix --skill ix-adversarial

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ix-adversarial
Source: https://github.com/GuitarAlchemist/ix/tree/main/.claude/skills/ix-adversarial
Command: npx skills add https://github.com/GuitarAlchemist/ix --skill ix-adversarial

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill enhances the robustness of machine learning models by enabling the testing of adversarial attacks and implementing defensive strategies.

Core Features & Use Cases

Adversarial Attack Testing: Evaluate model robustness against various attack techniques like FGSM, PGD, C&W, JSMA, and UAP.
Defensive Strategies: Implement defenses such as adversarial training, feature squeezing, statistical detection, and gradient regularization.
Poisoning Detection: Detect poisoned data using KNN label consistency, spectral signatures, and influence functions.
Privacy Enhancements: Apply differential privacy noise, confidence masking, and prediction purification for enhanced privacy.
Programmatic Usage: Utilize the Skill programmatically for custom applications in adversarial machine learning research and development.

Quick Start

Run the ix-adversarial skill to test the robustness of your model against adversarial examples.

Frequently Asked Questions about ix-adversarial

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I test my machine learning model against adversarial attacks?▼

To test your model against adversarial attacks, you can evaluate its robustness using techniques like FGSM, PGD, C&W, JSMA, and UAP to identify vulnerabilities and implement defensive strategies.

What is the best way to detect poisoned data in a machine learning dataset?▼

The best way to detect poisoned data is by analyzing label consistency using KNN, identifying spectral signatures, and applying influence functions to pinpoint anomalous data points manipulated to corrupt the model.

How does adversarial training improve model robustness?▼

Adversarial training improves model robustness by exposing the machine learning model to adversarial examples during the training phase, forcing it to learn stable features and resist malicious perturbations alongside standard defensive strategies.

Can I apply differential privacy noise to enhance model privacy?▼

Yes, you can apply differential privacy noise to enhance model privacy by adding calculated randomness to the data or model parameters, utilizing confidence masking and prediction purification to prevent information leakage.

What defensive strategies can I implement to protect my ML model from adversarial examples?▼

You can implement defensive strategies including feature squeezing to reduce input complexity, statistical detection to identify anomalies, gradient regularization to mask model sensitivity, and comprehensive adversarial training.

Do I need specialized frameworks to run adversarial machine learning testing scripts?▼

You can run adversarial machine learning testing programmatically using standard scripts without specialized frameworks, allowing custom application integration for research and development in defensive security and robustness evaluation.