Specialization

Adversarial Machine Learning and Information Security

Email

lovi.dhamija@thapar.edu

Education
1.    PhD in Computer Science and Engineering from Dr. B.R Ambedkar National Institute of Technology, Jalandhar.
o    Title – Design an efficient defensive framework against adversarial attacks.
2.    M.E(CSE-IS) from  PEC University of Technology, Chandigarh.
3.    B. Tech. in Computer Science and Engineering from Ganpati Institutions of Technology, YammunaNagar. 
4.    Schooling from SMS Sen Secondary School, Barara.

Trainings
1.    Certifications in various QIPs/FDPs/STC from organizations like IITs, NIT Jalandhar etc.

Experience
1.    Assistant Professor, Computer Science and Engineering Department, Thapar University Patiala, India (October - present)
2.    Assistant Professor, Computer Science and Engineering Department, PCTE institute of Engineering and Technology, Ludhiana (January 2025-September 2025)
3.    Teaching Assistant, Department of Electrical engineering and IT, Punjab Agricultural University, Ludhiana (September 2018- January 2020)

Journal publications
•    Lovi Dhamija and Urvashi Bansal, U. AFLF: a defensive framework to defeat multi-faceted adversarial attacks via attention feature fusion. Evolving Systems 16, 20 (2025). https://doi.org/10.1007/s12530-024-09643-z (Impact Factor-2.8, SCI Indexed)
•    Lovi Dhamija and Urvashi Bansal. How to Defend and Secure Deep Learning Models Against Adversarial Attacks in Computer Vision: A Systematic Review. New Gener. Comput. 42, 1165–1235 (2024). https://doi.org/10.1007/s00354-024-00283-0” (Impact Factor-2.0, SCI Indexed)
•    Lovi Dhamija and Urvashi Bansal. ”A Reliable Approach for generating realistic Adversarial Attacks”. Arabian Journal for Science and Engineering, Arab J Sci Eng 49, 13203–13220 (2024). https://doi.org/10.1007/s13369-024-09293-y (Impact Factor-2.6, SCI Indexed)
•    Lovi Dhamija and Urvashi Garg. "An adaptive randomized and secured approach against adversarial attacks." Information Security Journal: A Global Perspective 32.6 (2023): 401-416. (Impact Factor-1.6, ESCI Indexed)

Conference publications
•    Lovi Dua, and Divya Bansal. "Taxonomy: Mobile malware Threats and detection techniques." Computer Science & Information Technology (CS & IT) 213 (2014): 221.
•    Dhamija, L., Bansal, U. ”Evaluating the vulnerabilities of Deep Learning Architectures: A case study of VGGNet, ResNet50 and InceptionV3.” 6th International Conference on Deep Learning, Artificial Intelligence and Robotics. ”Published”

Peer reviewer for Journals
Discover Computing, Cluster computing

Honours and Achievements
●    Qualified National Eligibility Test (UGC-NET) for Assistant Professor in December 2018.