Tamer Abuhmed
Papers
2
Total Citations
5
H-Index
2
About
Tamer Abuhmed is a researcher whose work centers on the security and robustness of deep learning systems, with a particular focus on adversarial machine learning and the vulnerabilities inherent in modern AI models. His research addresses one of the most pressing challenges in applied artificial intelligence: the susceptibility of deep learning models to adversarial attacks — subtly manipulated inputs that can deceive even state-of-the-art systems while remaining imperceptible to human observers. Abuhmed's notable contributions include a comprehensive multi-dimensional examination of adversarial threats and defensive strategies, exploring the depth, breadth, and complexity of attack surfaces across critical real-world applications such as autonomous vehicles, surveillance systems, drones, and robotics. His 2022 work on adversarial attacks and defenses has already begun attracting scholarly attention with 3 citations, while his 2024 follow-up study continues to build on this foundation with 2 citations, reflecting an emerging and growing body of influence. By systematically mapping both offensive and defensive landscapes in deep learning security, Abuhmed's research provides valuable guidance for students, engineers, and policymakers working to deploy AI safely in high-stakes environments where model failures could have life-altering consequences.
Research Focus
Key Achievements
Top Papers
- 1
- 2