Shield your AI models from sophisticated adversarial attacks with cutting-edge defense mechanisms, robustness testing, and continuous monitoring designed for production environments.
Subtle input modifications that cause models to misclassify with high confidence, bypassing security systems and fraud detection.
Attackers query your model strategically to reverse-engineer its behavior and create functional copies of proprietary AI.
Hidden triggers embedded in models that activate malicious behavior when specific inputs are encountered in production.
A 2023 study showed that 87% of computer vision models are vulnerable to adversarial attacks, with autonomous vehicles misclassifying stop signs as speed limit signs and facial recognition systems failing to detect manipulated images. The financial impact averages $2.4M per successful attack in enterprise deployments.
We strengthen your models against attacks by training them on adversarial examples, making them inherently more robust:
Detect and neutralize adversarial inputs before they reach your model with advanced preprocessing:
Continuous surveillance of model behavior to identify attacks in real-time:
Design models with built-in security features that resist adversarial manipulation:
We rigorously test your models against known and novel attack vectors before deployment:
Full access to model architecture and parameters to generate strongest possible attacks:
Simulating real attacker scenarios with only query access to the model:
Testing resilience against real-world physical manipulation:
Validating training data integrity and detecting backdoors:
Get expert adversarial defense implementation and comprehensive robustness testing for your AI systems.
Adversarial attacks are carefully crafted inputs designed to fool AI models into making incorrect predictions. Unlike random noise, these attacks are imperceptible to humans but can completely break AI systems. For example, a stop sign with a small sticker could be misclassified as a speed limit sign by an autonomous vehicle, or a modified medical image could lead to incorrect diagnoses. If your business relies on AI for critical decisions, adversarial attacks pose serious security, safety, and liability risks.
Adversarial training significantly improves model robustness, typically reducing successful attack rates by 60-80% depending on the attack type and model architecture. However, it's not a silver bullet - models trained on specific attacks may still be vulnerable to novel attack methods. That's why we combine adversarial training with multiple defense layers including input sanitization, runtime monitoring, and architectural modifications for comprehensive protection.
There is typically a small accuracy-robustness tradeoff, with models losing 1-5% accuracy on clean data when hardened against adversarial attacks. However, we optimize this tradeoff through careful hyperparameter tuning, selective adversarial training, and ensemble methods. For most applications, this minor accuracy decrease is far outweighed by the security benefits and reduced risk of catastrophic failures in production.
Yes, we can add defensive layers to existing deployed models without requiring complete retraining. Our approach includes input preprocessing filters, confidence calibration, ensemble voting with robust models, and runtime monitoring systems. While the most robust solution involves retraining with adversarial examples, we can significantly improve security of existing models through wrapper defenses and monitoring infrastructure.
We maintain active research partnerships with academic institutions and continuously monitor the latest adversarial ML research. Our team participates in adversarial robustness competitions and publishes in top security conferences. We regularly update defense strategies based on emerging threats and conduct red team exercises to discover novel attack vectors before malicious actors do. All client systems receive quarterly security updates incorporating the latest defense techniques.
Comprehensive security for your entire machine learning infrastructure.
Safeguard your proprietary models from theft and unauthorized use.
Ensure your AI systems meet regulatory requirements and standards.
Identify vulnerabilities and assess risks in your AI deployment.
Don't let adversarial attacks compromise your AI systems. Contact Boaweb AI for comprehensive adversarial defense solutions and robustness testing.
Based in Lund, Sweden | Serving clients globally |