Adversarial Machine Learning and Defense Strategies 

Friday, February 24, 2023 - 01:00 pm
Storey Innovation Center, RM 2277 

Professor Dipankar Dasgupta 

Adversarial attacks can disrupt artificial intelligence (AI) and machine learning (ML) based system functionalities but also provide significant research opportunities. In this talk, Prof Dipankar Dasgupta from The University of Memphis will cover emerging adversarial machine learning (AML) attacks on systems and the state-of-the-art defense techniques. Prof Dasgupta will first discuss how and where adversarial attacks could happen in an AI/ML model and framework. He will then present the classification of adversarial attacks and their severity and applicability in real-world problems, including the steps to mitigate their effects, before illustrating the role of GAN in adversarial attacks and as a defense strategy. 

Finally, Prof Dasgupta will also discuss a dual-filtering (DF) strategy that could mitigate adaptive or advanced adversarial manipulations for a wide-range of ML attacks with higher accuracy. The developed DF software could be used as a wrapper to any existing ML-based decision support system to prevent a wide variety of adversarial evasion attacks. The DF framework utilizes two sets of filters based on positive (input filters) and negative (output filters) verification strategies that could communicate with each other for higher robustness. 


  • Dasgupta, D., Gupta, K.D. Dual-filtering (DF) schemes for learning systems to prevent adversarial attacks. Complex Intell. Syst. (2022).  
  • Gupta, K., & Dasgupta, D. Who is Responsible for Adversarial Defense? Workshop on Challenges in Deploying and monitoring Machine Learning Systems, (ICML 2021).  
  • K. D. Gupta, D. Dasgupta and Z. Akhtar, "Applicability issues of Evasion-Based Adversarial Attacks and Mitigation Techniques," 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia, 2020, pp. 1506-1515, doi: 10.1109/SSCI47803.2020.9308589. 

Dr. Dipankar Dasgupta is a professor of Computer Science at the University of Memphis since 1997, an IEEE Fellow, an ACM Distinguished Speaker (2015-2020) and an IEEE Distinguished Lecturer (2022-2024). Dr. Dasgupta is known for his pioneering work on the design and development of intelligent solutions inspired by natural and biological processes. During 1990-2000, he extensively studied different AI/ML techniques and research in the development of an efficient search and optimization method (called structured genetic algorithm) has been applied in engineering design, neural-networks, and control systems. He is one of the founding fathers of the field of artificial immune systems (a.k.a Immunological Computation) and is at the forefront of applying bio-inspired approaches to cyber defense. His notable works in digital immunity, negative authentication, cloud insurance modeling, dual-filtering and adaptive multi-factor authentication demonstrated the effective use of various AI/ML algorithms. His research accomplishments and achievements have appeared in Computer World Magazine, NASA’s website, and in local TV Channels and Newspapers. 

Dr. Dasgupta has authored four books, 5 patents (including 2 under submissions) and has more than 300 research publications (20,000 citations as per google scholar) in book chapters, journals, and international conference proceedings. Among many awards, he was honored with the 2014 ACM-SIGEVO Impact Award for his seminal work on negative authentication, an AI-based approach. He also received five best paper awards in different international conferences and has been organizing IEEE Symposium on Computational Intelligence in Cyber Security at SSCI since 2007. Dr. Dasgupta is an ACM Distinguished Speaker, regularly serves as panelist and keynote speaker and offers tutorials in leading computer science conferences and have given more than 350 invited talks in different universities and industries.