{"product_id":"kit-pi-5-adversarial-attack-research-kit","title":"Pi 5 Adversarial Attack Research Kit","description":"\u003ch1\u003eRaspberry Pi 5 Adversarial Attack Research Kit – Expose TFLite Model Brittleness with FGSM \u0026amp; PGD\u003c\/h1\u003e\n\u003cp class=\"value-summary\"\u003eEvery part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.\u003c\/p\u003e\n\u003cdiv class=\"specs-strip\"\u003e\n  \u003cspan\u003e\u003cstrong\u003eDifficulty:\u003c\/strong\u003e Advanced\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eBuild Time:\u003c\/strong\u003e 10-12 hrs\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eAge:\u003c\/strong\u003e 18-25\u003c\/span\u003e\n  \u003cspan\u003e\u003cstrong\u003eSkill:\u003c\/strong\u003e Adversarial ML \u0026amp; Edge AI Security\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cp\u003eThis 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.\u003c\/p\u003e\n\u003ch2\u003eWhat You'll Build\u003c\/h2\u003e\n\u003cp\u003eYou’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.\u003c\/p\u003e\n\u003ch2\u003eWhat You'll Learn\u003c\/h2\u003e\n\u003cul\u003e\n  \u003cli\u003eImplement FGSM and PGD attack algorithms in Python and execute them against a quantized TensorFlow Lite model\u003c\/li\u003e\n  \u003cli\u003eDeploy and benchmark a computer vision classifier on Raspberry Pi 5 with NVMe‑accelerated storage\u003c\/li\u003e\n  \u003cli\u003eQuantify adversarial robustness using top‑1 accuracy drop, perturbation SNR, and visual distortion metrics\u003c\/li\u003e\n  \u003cli\u003eAnalyse practical defence strategies and trade‑offs between clean‑data accuracy and adversarial resilience\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2\u003eKit Contents\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eComponent\u003c\/th\u003e\n\u003cth\u003eQuantity\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eRaspberry Pi 5 8GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eNVMe SSD 512GB\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003ePi 5 M.2 HAT+\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eUSB-C PSU\u003c\/td\u003e\n\u003ctd\u003e1\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eWhy Buy This Kit Instead of Sourcing Parts Separately\u003c\/h2\u003e\n\u003ctable\u003e\n  \u003cthead\u003e\u003ctr\u003e\n\u003cth\u003eFactor\u003c\/th\u003e\n\u003cth\u003eSourcing Separately\u003c\/th\u003e\n\u003cth\u003eCompoden Kit\u003c\/th\u003e\n\u003c\/tr\u003e\u003c\/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n\u003ctd\u003eCompatibility checks\u003c\/td\u003e\n\u003ctd\u003eYou verify every part\u003c\/td\u003e\n\u003ctd\u003ePre-tested as a system\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eBuild support\u003c\/td\u003e\n\u003ctd\u003eForums and scattered tutorials\u003c\/td\u003e\n\u003ctd\u003eAI companion trained on this exact project\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eTime to first working build\u003c\/td\u003e\n\u003ctd\u003eDays of debugging\u003c\/td\u003e\n\u003ctd\u003eHours, with step-by-step guidance\u003c\/td\u003e\n\u003c\/tr\u003e\n    \u003ctr\u003e\n\u003ctd\u003eShipping coordination\u003c\/td\u003e\n\u003ctd\u003eMultiple sellers, multiple delays\u003c\/td\u003e\n\u003ctd\u003eOne shipment from Bengaluru in 3-5 days\u003c\/td\u003e\n\u003c\/tr\u003e\n  \u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ch2\u003eWho This Kit Is For\u003c\/h2\u003e\n\u003cp\u003eIdeal 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.\u003c\/p\u003e\n\u003ch2\u003eBuilt and Backed by Compoden\u003c\/h2\u003e\n\u003cp\u003eEvery 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.\u003c\/p\u003e\n\u003cdetails\u003e\u003csummary\u003eWhat if I get stuck during the build?\u003c\/summary\u003e\u003cp\u003eScan the QR code inside the box to chat with the AI companion trained specifically on this Raspberry Pi 5 adversarial attack project. You can also send a WhatsApp message to our support team for personalised guidance if the companion doesn't resolve your issue.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDo I need prior knowledge of adversarial machine learning?\u003c\/summary\u003e\u003cp\u003eThe scripts and build guide explain FGSM and PGD step by step, but solid familiarity with Python, TensorFlow, and the Linux command line is expected. If the theory is new to you, we recommend pairing the build with a foundational paper on adversarial examples.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eCan I attack my own custom TFLite model?\u003c\/summary\u003e\u003cp\u003eYes. The provided attack scripts accept any quantised TFLite image classification model. Just drop your .tflite file and class labels into the designated folder; the pipeline automatically loads and attacks it.\u003c\/p\u003e\u003c\/details\u003e\n\u003cdetails\u003e\u003csummary\u003eDoes the kit include the target classifier and images?\u003c\/summary\u003e\u003cp\u003eIt does. A pre‑trained MobileNetV2 TFLite model and a set of sample images from ImageNet are pre‑loaded on the SSD, so you can start generating adversarial examples immediately after assembly.\u003c\/p\u003e\u003c\/details\u003e\n\n\u003cdiv class=\"kit-description\"\u003e\n  \u003cp\u003eFGSM and PGD adversarial examples generated on Pi 5 fool deployed TFLite classifiers — robustness analysis research.\u003c\/p\u003e\n  \u003ch4\u003eWhat's in this kit\u003c\/h4\u003e\n  \u003cul\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-model-b-8gb-high-performance-single-board-computer\"\u003eRaspberry Pi 5 8GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/official-raspberry-pi-m2-hat-nvme-ssd-add-on-board-for-pi-5\"\u003eNVMe SSD 512GB\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-5-pcie-to-m2-nvme-ssd-expansion-board-by-elecrow\"\u003ePi 5 M.2 HAT+\u003c\/a\u003e\u003c\/li\u003e\n    \u003cli\u003e\u003ca href=\"\/products\/raspberry-pi-4-official-power-supply-5v-3a-usb-c-compoden\"\u003eUSB-C PSU\u003c\/a\u003e\u003c\/li\u003e\n  \u003c\/ul\u003e\n\u003c\/div\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is included in the Pi 5 Adversarial Attack Research Kit?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The Pi 5 Adversarial Attack Research Kit includes all components needed: Raspberry Pi 5 8GB, NVMe SSD 512GB, Pi 5 M.2 HAT+, USB-C PSU and more. Everything is pre-tested for compatibility and shipped from Bengaluru, India.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What skill level is required for the Pi 5 Adversarial Attack Research Kit?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"This kit is designed for Advanced level makers, suitable for ages 18-25. FGSM and PGD adversarial examples generated on Pi 5 fool deployed TFLite classifiers — robustness analysis research. Estimated build time is 10-12 hrs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I buy the Pi 5 Adversarial Attack Research Kit online in India?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, the Pi 5 Adversarial Attack Research Kit is available online at Compoden (compoden.in), India's AI-powered electronics and robotics store. Ships from Bengaluru in 1-5 business days across India.\"\n      }\n    }\n  ]\n}\n\u003c\/script\u003e\n\n\u003cscript type=\"application\/ld+json\"\u003e\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"Product\",\n  \"name\": \"Pi 5 Adversarial Attack Research Kit\",\n  \"description\": \"FGSM and PGD adversarial examples generated on Pi 5 fool deployed TFLite classifiers — robustness analysis research.\",\n  \"sku\": \"CDN-KIT-2577\",\n  \"brand\": {\"@type\": \"Brand\", \"name\": \"Compoden\"},\n  \"offers\": {\n    \"@type\": \"Offer\",\n    \"url\": \"https:\/\/compoden.in\/products\/kit-pi-5-adversarial-attack-research-kit\",\n    \"priceCurrency\": \"INR\",\n    \"price\": \"50550\",\n    \"availability\": \"https:\/\/schema.org\/InStock\",\n    \"seller\": {\"@type\": \"Organization\", \"name\": \"Compoden\"}\n  },\n  \"category\": \"Edge AI \u0026 Computer Vision\"\n}\n\u003c\/script\u003e","brand":"Compoden","offers":[{"title":"Default Title","offer_id":53469370581357,"sku":"CDN-KIT-2577","price":59650.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0999\/3997\/5533\/files\/kit-pi-5-adversarial-attack-research-kit.png?v=1781948432","url":"https:\/\/compoden.com\/products\/kit-pi-5-adversarial-attack-research-kit","provider":"Compoden","version":"1.0","type":"link"}