Raspberry Pi 5 Adversarial Attack Research Kit – Expose TFLite Model Brittleness with FGSM & PGD

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

This kit empowers researchers and advanced makers to recreate real‑world adversarial attacks like the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) on a live TensorFlow Lite classifier running on a Raspberry Pi 5. You’ll generate image perturbations imperceptible to humans yet capable of forcing misclassification, then quantify how brittle the model really is – a foundational step for building robust, secure edge AI that stands up to adversarial manipulation.

What You'll Build

You’ll assemble a self-contained edge AI testing station: a Pi 5 booting from an NVMe SSD for fast inference and attack script execution. With the included scripts, you’ll apply white‑box FGSM and iterative PGD attacks on a pre‑trained TFLite MobileNetV2 model, observing confidence scores crash and class labels flip. By varying epsilon and iteration steps, you can map out decision boundaries and document robustness curves, producing publishable data for a conference paper, hackathon submission, or B.Tech final‑year project.

What You'll Learn

• Implement FGSM and PGD attack algorithms in Python and execute them against a quantized TensorFlow Lite model
• Deploy and benchmark a computer vision classifier on Raspberry Pi 5 with NVMe‑accelerated storage
• Quantify adversarial robustness using top‑1 accuracy drop, perturbation SNR, and visual distortion metrics
• Analyse practical defence strategies and trade‑offs between clean‑data accuracy and adversarial resilience

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

Ideal for B.Tech ECE/CSE/AI students pursuing final‑year projects in adversarial machine learning, Smart India Hackathon teams prototyping secure computer vision pipelines, and IIT/NIT/VIT/BITS researchers validating edge AI robustness for publication. Security‑conscious makers who want to move beyond toy examples and experiment with real attack vectors on embedded hardware will also find this kit a ready‑to‑run platform.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible via a QR code on the box, with WhatsApp and email backup. We've spent 10 years building projects for makers, schools, and institutions across India. If a part fails because of a manufacturing defect, replace it free within 7 days.