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About

Ihsen Alouani is a leading researcher at the intersection of adversarial machine learning and autonomous systems, with a primary focus on the security vulnerabilities of monocular depth estimation (MDE) in navigation applications. His major contributions center on developing novel adversarial attack methodologies that expose critical weaknesses in CNN- and Transformer-based depth perception models. Alouani pioneered the concept of shape-sensitive adversarial patches, most notably through his work on SSAP (Shape-Sensitive Adversarial Patch), which demonstrates how carefully crafted physical patches can comprehensively disrupt depth estimation in real-world autonomous driving scenarios. His research, including the APARATE framework, has accumulated significant attention, with his most cited papers garnering over a dozen citations in just their first year of publication. These works are particularly impactful because they address a critical safety gap: while MDE systems have achieved remarkable performance through deep learning, Alouani’s research reveals their dangerous susceptibility to physical-world attacks. His findings have direct implications for the safety and robustness of autonomous vehicles, robotics, and any system relying on depth perception for navigation. Alouani’s work represents a vital contribution to making AI-driven autonomous systems more resilient against adversarial threats.