Robust Physical-World Attacks on Machine Learning Models

This article likely discusses vulnerabilities in machine learning models when deployed in real-world scenarios. It suggests that these models can be tricked or manipulated by physical attacks, highlighting the importance of security considerations in AI development and deployment. The 'Robust' in the title implies the attacks are designed to be effective even under varying conditions.

• •Machine learning models are susceptible to physical-world attacks.
• •The attacks are designed to be robust, meaning they are effective under various conditions.
• •Security is a critical consideration for AI development and deployment.