NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs
NERO-Net: A Neuroevolutionary Approach for the Design of Adversarially Robust CNNs explores A neuroevolutionary approach to design CNNs that are intrinsically robust to adversarial attacks, improving post-attack accuracy without sacrificing clean accuracy.. Commercial viability score: 3/10 in AI Model Design.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisDetection of Adversarial Attacks in Robotic PerceptionViability: 2.0
- Higher ViabilityrSDNet: Unified Robust Neural Learning against Label Noise and Adversarial AttacksViability: 4.0
- Higher ViabilityEfficient Preemptive Robustification with Image SharpeningViability: 4.0
- Higher ViabilityBrain Tumor Classifiers Under Attack: Robustness of ResNet Variants Against Transferable FGSM and PGD AttacksViability: 5.0
- Higher ViabilitySECURE: Stable Early Collision Understanding via Robust Embeddings in Autonomous Driving