Harden Your AI Against Adversarial Examples

Even high-performing AI can be deceived.
Test your resilience in the lab, before your model gets deployed.

Why Robustness Matters

Even the best AI models can be fooled. Adversarial examples are malicious inputs created to deceived your AI that can lead your system to make critical mistakes.

What Is an Adversarial Example?

Adversarial examples are inputs designed to fool AI models. They can work on any kind of data, such as images, text or audio. They include perturbations that push the model to make incorrect predictions.

For example, an image of a stop sign might be modified so that an AI sees a speed limit sign instead. In natural language processing, a small word tweak might cause a sentiment classifier to flip from “positive” to “negative.” These attacks exploit the fact that many AI models learn patterns that are statistically useful but semantically fragile.

Clickable image (photography of Skyld's CEO Marie Paindavoine) that link to a youtube video

Our skills, your benefits

Model Robustness

Strengthen your models against adversarial attacks.

Model Reliability

Your model will be reliable in real-world scenarios.

Regulatory Readiness

Meet regulatory requirements and industry standards.

Actionable insights

Get detailed diagnostics and tailored recommendations.

Multiple Modalities

Test available for image recognition, audio, NLP, or tabular data.

Unmatched Confidence

Build an AI that remains reliable even under the most advanced attacks.

Image representing an adversarial example attack.

What We Offer

We simulate real-world adversarial attacks on your AI models to expose blind spots and evaluate robustness. Our service provides detailed diagnostics, tailored recommendations, and actionable insights to strengthen your models against manipulation. Whether you work on image recognition, NLP, or tabular data, our testing suite adapts to your use case and helps you build truly reliable AI

Is it possible to detect and filter an adversarial example?

We are working on it. Want to test in beta? Get in touch!

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FAQ

An adversarial example is a malicious input designed to deceive AI models. It can lead to incorrect predictions, which can be critical in applications like autonomous driving or healthcare.

We conduct adversarial testing to identify vulnerabilities in your model. This includes generating adversarial examples and evaluating the model’s performance against them.

Adversarial examples can affect any type of model, including image, text, and tabular data. Our testing suite is designed to evaluate robustness across multiple modalities.

While it’s impossible to make a model completely immune, we can significantly enhance its robustness. Our service provides tailored recommendations to strengthen your model against adversarial attacks.

Our robustness testing process includes generating adversarial examples, evaluating your model’s performance against them, and providing detailed diagnostics and recommendations for improvement.

The results will identify vulnerabilities in your model and provide actionable insights to enhance its robustness. This helps ensure that your AI system performs reliably in real-world scenarios.

Do you have questions about your AI model? Our expert team is here to help. Whether you need advice on best practices, have specific challenges to discuss, or want guidance on integrating our solution, we are with you every step of the way. Let’s schedule a meeting.

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