Term
Adversarial Attack (ˌæd.vəˈseə.ri.əl əˈtæk)
Definition
Adversarial Attacks are deliberate attempts to confuse or mislead AI systems by feeding them manipulated inputs, resulting in the AI making mistakes or wrong decisions.
Where you’ll find it
Adversarial Attacks can occur in any AI system that processes input data to make decisions. This includes everything from machine learning models in cybersecurity defenses to AI-driven algorithms in apps and software.
Common use cases
- Testing the ability of AI systems to handle unexpected or malicious inputs.
- In cybersecurity, strengthening AI systems by identifying and correcting vulnerabilities.
- Researchers demonstrate the need for improvements in AI safety and accuracy.
Things to watch out for
- Adversarial attacks can be subtle and hard to detect, making them a significant risk for automated systems.
- They may lead to incorrect AI decisions, which can be exploited in harmful ways if not addressed.
- Adequate testing and updates are necessary to protect AI systems against these attacks.
Related terms
- Machine Learning
- AI Vulnerability
- Cybersecurity
- Robustness Testing
- Input Preprocessing
Pixelhaze Tip: To safeguard your AI systems against adversarial attacks, consistently update and test them using the latest security tools and techniques. Keeping your AI models well-trained on diverse and challenging datasets can also minimize risks.