Physical Attacks on AI Systems
Physical attacks manipulate the real-world environment to fool AI systems, posing serious threats to autonomous vehicles, surveillance systems, and robotics.
Physical attacks on AI systems represent a critical security concern as AI becomes increasingly deployed in real-world applications. Unlike digital adversarial examples that exist only in pixel space, physical attacks involve manipulating actual objects, environments, or conditions that AI systems encounter in the physical world. These attacks are particularly dangerous because they can be executed by anyone with physical access to the target environment, requiring no technical expertise in AI or access to model internals.
The challenge of physical attacks lies in the domain gap between digital and physical spaces. Attackers must account for factors such as lighting conditions, camera angles, distance, and environmental noise when crafting physical adversarial examples. However, research has demonstrated that physical attacks can be highly effective, with adversarial patches achieving success rates exceeding 90% in some scenarios. This effectiveness, combined with the ease of execution, makes physical attacks a realistic and serious threat to deployed AI systems.
Physical attacks have been demonstrated against various AI systems including autonomous vehicles, facial recognition systems, object detection in security cameras, and robotic vision systems. The consequences of successful physical attacks can be severe, ranging from safety hazards in autonomous systems to security bypasses in access control. Understanding physical attack techniques and implementing robust defenses is essential for securing AI systems deployed in physical environments.
Adversarial Patches
Printed stickers or patterns that cause misclassification when placed in camera view
3D Adversarial Objects
Physical objects designed to be misclassified from multiple viewing angles
Environmental Manipulation
Modifying lighting, shadows, or backgrounds to fool perception systems
- Autonomous Vehicles: Stop sign misclassification, lane detection evasion
- Surveillance Systems: Face recognition bypass, person detection evasion
- Access Control: Biometric authentication spoofing
- Robotics: Object recognition manipulation, navigation disruption
Stop Sign Attack
Researchers demonstrated that small stickers on stop signs could cause autonomous vehicles to misclassify them as speed limit signs
Turtle-Rifle Misclassification
3D-printed turtle designed to be classified as a rifle from any viewing angle by image classifiers
Adversarial Glasses
Specially designed eyeglass frames that can fool facial recognition systems or enable impersonation
Detection Methods
- • Multi-sensor fusion for verification
- • Temporal consistency checking
- • Anomaly detection in predictions
- • Context-aware validation
Robustness Techniques
- • Adversarial training with physical examples
- • Input preprocessing and filtering
- • Ensemble models with diverse architectures
- • Certified defenses for physical perturbations