Battista Biggio
Papers
1
Total Citations
77
H-Index
1
About
Battista Biggio is a leading figure in the field of adversarial machine learning, where his work has fundamentally shaped how we understand the security and robustness of AI systems. His research spans the intersection of computer security, pattern recognition, and deep learning, with a particular focus on how intelligent systems can be attacked and defended. Biggio is perhaps best known for pioneering the study of adversarial examples in physical, real-world systems—a line of inquiry exemplified by his highly influential paper "Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid" (2018, 77 citations). This work demonstrated that even sophisticated deep neural networks deployed on humanoid robots could be fooled by carefully crafted visual perturbations, raising critical safety concerns for autonomous systems. Beyond this, his broader contributions to the theory of adversarial machine learning have earned him thousands of citations, establishing him as one of the most cited researchers in the field. A professor at the University of Cagliari, Biggio’s research continues to inform the design of more robust and trustworthy AI, making him an essential voice for any student or researcher concerned with the security of modern machine learning.
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Top Papers
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