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AI Security Simulator

Interactive AI security simulator: launch adversarial (FGSM) attacks, data poisoning and model-extraction attacks against a neural network classifier, then defend it with adversarial training and differential-privacy noise.

AI & Machine Learning3DAdvanced60 FPS

Details

CategoryAI & Machine Learning

RenderingThree.js · WebGL

DifficultyAdvanced

Target FPS60

ControlsAttack-type buttons (None / Adversarial / Poisoning / Extraction) switch the active threat model; attack-strength slider (epsilon) sets perturbation/poisoning magnitude; adversarial-training toggle widens the decision margin as a defense; privacy-budget slider (epsilon_DP) adds differential-privacy noise; drag to orbit, scroll to zoom.

Three.jsAI securityadversarial examplesdifferential privacyInstancedMesh

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AI security covers the defenses that keep machine-learning models trustworthy once they leave the lab: resisting adversarial examples crafted to fool a classifier, resisting data poisoning that corrupts training data to bend a model's decisions, resisting model-extraction attacks that clone a proprietary model through its API, and protecting the privacy of the data the model was trained on. This simulator visualizes a small neural classifier and a two-class data cloud in 3D, lets you launch each attack family against it, and lets you switch on adversarial training and differential-privacy noise to see the accuracy/security/privacy trade-offs play out live.

⚙ Under the hood

Launch adversarial (FGSM), data-poisoning and model-extraction attacks against a neural network classifier, then defend it with adversarial training and differential-privacy noise, watching accuracy, attack success and privacy risk change live.

Three.jsAI securityadversarial examplesdifferential privacyInstancedMesh

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install