ANTIDOTE: AI Attack and Defense for the Smart Healthcare

ANTIDOTE is a groundbreaking initiative aimed at enhancing the security and resilience of AI models in the smart healthcare sector. By addressing adversarial threats, it ensures trustworthy and safe AI-driven medical solutions, protecting sensitive healthcare data and improving patient outcomes.

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Main Objective

The objective of the project is to create a sustainable European and intersectoral network of organizations working on a joint research programme in the interdisciplinary fields of Healthcare, AI and Cybersecurity. The participants will exchange skills and knowledge which will allow them to design and develop concrete mechanisms to evaluate the robustness of AI models and propose novel methods to ensure their secure, safe, resilient and trustworthy operation in the healthcare domain.

The Consortium

Contribution

The ANTIDOTE project will be evaluated through three pilot demonstrations, each focusing on different cybersecurity scenarios. These pilots will test the effectiveness of AI-driven defensive and offensive mechanisms in adversarial settings.

Objective 1: Design an AI Threat Model for Smart Healthcare.
This objective aims to create an AI-driven threat model for the smart healthcare sector, focusing on adversarial AI and medical cybersecurity. It includes reviewing existing techniques and applying them to areas like medical image analysis and IoT security. The model will address different attack scenarios and evaluate their impact on healthcare systems' security.

Objective 2: Deliver AI-Driven Services for Secure Healthcare.
The goal is to develop AI services for securing medical data and devices. This includes federated AI for medical image diagnosis, deep learning malware detection for medical IoT, and AI-powered intrusion detection systems for IoT networks to protect against cyberattacks.

Objective 3: Innovate Adversarial AI Techniques for Security Evaluation.This objective focuses on creating novel adversarial AI techniques to test the security of AI models in healthcare. It involves developing methods to create adversarial medical images and bypass malware and IDS classifiers, thus stress-testing the robustness of AI systems.