Author: Xabier Etxeberria Barrio / VICOM
In an increasingly interconnected world, where Artificial Intelligence (AI) is rapidly becoming an essential component in critical sectors such as healthcare, chemical plants, energy systems, and cybersecurity, ensuring the reliability and security of these AI systems is of utmost importance. The rise in AI adoption, while offering numerous advantages, also exposes systems to potential vulnerabilities that can compromise their functionality and trustworthiness.
To address these emerging challenges, the KINAITICS project has developed NeuralSentinel, an advanced tool designed to validate and enhance the reliability and trustworthiness of Artificial Neural Networks (ANNs). NeuralSentinel offers a comprehensive solution that combines adversarial attack and defense strategies with explainability (XAI) techniques, enabling users to test, stress, and safeguard AI models from malicious threats and performance anomalies.
Key Features of NeuralSentinel:
- Adversarial Attack and Defense: NeuralSentinel allows users to apply various state-of-the-art adversarial attacks, such as the Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Projected Gradient Descent (PGD), to stress-test AI models. It also offers robust defense mechanisms such as adversarial training, dimensionality reduction, and prediction similarity to protect models from these attacks, ensuring that the AI system remains reliable under real-world conditions.
- Explainability and Trust: NeuralSentinel incorporates an explainability module (XAI), which provides insights into how the AI model makes decisions. This feature helps both technical experts and non-expert users to understand the inner workings of the model, increasing confidence in AI decisions. By monitoring key neurons and tracking their behavior during model attacks, NeuralSentinel offers valuable transparency, helping users identify and address vulnerabilities in neural networks.
- User-Friendly Interface: NeuralSentinel provides a simple and intuitive interface that allows users to easily load, test, and visualize the effects of adversarial attacks and defenses on AI models. The interface also includes detailed visual reports showing original, attacked, and defended data, offering clarity on the impact of each action.
Relevant Environment Validation
NeuralSentinel has been successfully validated through its application in a healthcare use case during a Hackathon organized by the KINAITICS project. The tool was used to evaluate the reliability and trustworthiness of an AI model designed to detect skin cancer from medical images. Participants, including both AI experts and non-experts, were able to test the model’s resilience by applying adversarial attacks and implementing defense strategies. This hands-on experience helped participants understand the importance of securing AI models in critical applications and provided valuable feedback for improving the tool’s functionality.
The event highlighted the significant benefits of combining adversarial testing with explainability to build trust in AI systems. Moreover, it demonstrated the importance of ensuring that AI models used in sensitive fields like healthcare are thoroughly tested and safeguarded to prevent misclassifications that could endanger human lives.
A Step Toward Trustworthy AI
NeuralSentinel represents a critical step forward in enhancing the security, reliability, and trustworthiness of AI systems. By offering users the tools to test, understand, and protect their AI models, it supports the broader goal of developing AI systems that are safe, reliable, and transparent in their decision-making processes.
As AI continues to be integrated into critical infrastructures and decision-making processes, the need for tools like NeuralSentinel will only grow. Through its continued development and improvement, NeuralSentinel will remain at the forefront of ensuring AI systems operate with the highest levels of reliability and trust, safeguarding both the digital and physical environments they influence.