Giorgio Fumera
Papers
2
Total Citations
100
H-Index
2
About
Giorgio Fumera is a leading researcher in the fields of computer vision, machine learning, and adversarial machine learning. His work critically examines the security and reliability of deep neural networks, particularly in high-stakes applications like robotics and video surveillance. Fumera’s major contributions include pioneering research on adversarial examples against robotic vision systems, as demonstrated in his highly cited 2018 paper on the iCub humanoid (77 citations), which revealed how barely-perceivable image alterations can fool deep learning models. He has also advanced the use of synthetic data for surveillance, authoring a comprehensive 2024 review (23 citations) that explores its potential for controlled experimentation and robust model training. Fumera’s impact is evident in his work’s influence on both academic research and practical safety considerations for AI systems. His notable achievements include bridging the gap between theoretical vulnerabilities and real-world robotic platforms, helping to shape the discourse on trustworthy AI. For students and researchers, Fumera’s research offers critical insights into the fragility of deep learning and the innovative use of synthetic data to build more resilient computer vision systems.
Research Focus
Key Achievements
Top Papers
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- 2