Abhijeet Solanki

I am an AI Researcher focused on adversarial robustness, trustworthy AI, and perception security for autonomous systems. I recently earned my Master of Science in Computer Engineering with a concentration in AI from Tennessee Technological University, where I was advised by Dr. Syed Rafay Hasan. My work spans adversarial machine learning, LiDAR and camera-based perception, edge AI deployment, and real-time defense mechanisms for safety-critical environments. Recently, I have investigated adversarial machine learning vulnerabilities in Ouster LIDAR and camera sensor fusion, engineering novel defenses optimized for edge devices like Nvidia Jetson. I am actively interested in pursuing a PhD in robust machine learning, secure autonomy, and trustworthy multimodal perception.

Currently I work on adversarial robustness and trustworthy AI , focusing heavily on perception security for autonomous systems. I also work on using adversarial machine learning for LiDAR and camera-based perception to deploy real-time defense mechanisms in safety-critical edge AI environments. Furthermore, in my industry role, I build GenAI-powered troubleshooting assistants using Python and Azure OpenAI to automate diagnostics and improve incident response velocity.

In the past, I have worked on advances in secure electronic health records by architecting decentralized platforms using Docker containerization and an IPFS cluster. Additionally, I have spent time developing early-stage deep learning pipelines for image classification, as well as engineering scalable backend systems and distributed ETL pipelines for high-traffic applications.

News

Dec, 2025	Graduated with a Master of Science in Computer Engineering from Tennessee Technological University.
Sep, 2025	Started a new position as a Senior Software Engineer at Arctera.
Aug, 2025	Accepted at IEEE MWSCAS: “Realistic GPS Spoofing via Customized CARLA GPS Navigation and Controller Systems”.
Jun, 2025	Accepted at IEEE Access: “Survey of Navigational Perception Sensors’ Security in Autonomous Vehicles”.
May, 2025	Submitted paper to NeurIPS 2025.

Selected publications

ACCESS
Survey of Navigational Perception Sensors’ Security in Autonomous Vehicles

Abhijeet Solanki, W. Al Amiri, M. Mahmoud, B. Swieder, S. R. Hasan, and T. N. Guo

IEEE Access Jun 2025

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This paper presents a comprehensive survey of the security vulnerabilities and attack vectors associated with navigational perception sensors in autonomous vehicles (AVs). We analyze various types of sensors, including LiDAR, radar, cameras, and GPS, and discuss the potential threats they face from adversarial attacks. The survey categorizes attacks based on their target sensor and attack methodology, providing insights into the motivations behind these attacks and their potential impact on AV safety. We also review existing defense mechanisms and propose future research directions to enhance the security of navigational perception systems in AVs.
@article{solanki2025survey, title = {Survey of Navigational Perception Sensors' Security in Autonomous Vehicles}, author = {Solanki, Abhijeet and Al Amiri, W. and Mahmoud, M. and Swieder, B. and Hasan, S. R. and Guo, T. N.}, journal = {IEEE Access}, pages = {1--29}, month = jun, year = {2025}, publisher = {IEEE}, doi = {10.1109/ACCESS.2025.3578891}, }
ACCESS
Blinded by the Beam: A Unified Real-Time Defense Against Laser-Based Attacks on Navigational Perception of Autonomous Vehicles

Abhijeet Solanki, W. Al Amiri, S. R. Hasan, and T. N. Guo

IEEE Access Jun 2026

Abs Bib

This paper presents a unified, real-time defense against laser-based attacks that blind the camera and LiDAR perception of autonomous vehicles. We characterize how laser interference degrades navigational perception and introduce a lightweight detection-and-mitigation mechanism that neutralizes attacks before they reach the navigation stack, while remaining efficient enough to run directly on in-vehicle edge hardware such as the NVIDIA Jetson Orin and Nano. Experiments demonstrate reliable real-time detection with low latency, preserving perception integrity under adversarial physical-world conditions.
@article{solanki2026blinded, title = {Blinded by the Beam: A Unified Real-Time Defense Against Laser-Based Attacks on Navigational Perception of Autonomous Vehicles}, author = {Solanki, Abhijeet and Al Amiri, W. and Hasan, S. R. and Guo, T. N.}, journal = {IEEE Access}, volume = {14}, pages = {57235--57255}, year = {2026}, publisher = {IEEE}, }
SOUTHEASTCON
ReAL: Machine Learning Detection of Reflective Attacks Against Lidarometry

Abhijeet Solanki, L. Beirne, S. R. Hasan, and W. Al Amiri

In SoutheastCon 2025 Mar 2025

Abs Bib Code

This paper introduces ReAL, a machine learning-based approach for detecting reflective attacks against lidarometry in autonomous vehicles. Reflective attacks involve the manipulation of lidar signals to create false perceptions of the environment, posing significant risks to AV safety. ReAL utilizes a combination of feature extraction and classification techniques to identify anomalies in lidar data that may indicate the presence of reflective attacks. We evaluate the performance of ReAL using a dataset of simulated and real-world lidar data, demonstrating its effectiveness in accurately detecting reflective attacks while minimizing false positives. The results suggest that ReAL can serve as a valuable tool for enhancing the security of lidar-based perception systems in autonomous vehicles.
@inproceedings{solanki2025real, title = {ReAL: Machine Learning Detection of Reflective Attacks Against Lidarometry}, author = {Solanki, Abhijeet and Beirne, L. and Hasan, S. R. and Al Amiri, W.}, booktitle = {SoutheastCon 2025}, pages = {1309--1313}, month = mar, year = {2025}, organization = {IEEE}, doi = {10.1109/SoutheastCon56624.2025.10971487}, }
SOUTHEASTCON
GNAPing On the Job: Attacking and Defending Facial Detection on Edge Devices

Abhijeet Solanki, R. T. Thornton, S. R. Hasan, and U. Qidwai

In SoutheastCon 2025 Mar 2025

Abs Bib Code

This paper presents GNAPing, a novel approach for attacking and defending facial detection on edge devices. GNAPing exploits vulnerabilities in facial detection algorithms to create adversarial examples that can bypass detection systems. We evaluate the effectiveness of GNAPing on various edge devices, demonstrating its ability to successfully evade facial detection while maintaining high visual fidelity. Additionally, we propose defense mechanisms to mitigate the impact of GNAPing attacks, including adversarial training and input preprocessing techniques. Our experimental results show that these defenses can significantly reduce the success rate of GNAPing attacks, enhancing the security of facial detection systems on edge devices.
@inproceedings{solanki2025gnaping, title = {GNAPing On the Job: Attacking and Defending Facial Detection on Edge Devices}, author = {Solanki, Abhijeet and Thornton, R. T. and Hasan, S. R. and Qidwai, U.}, booktitle = {SoutheastCon 2025}, pages = {918--923}, month = mar, year = {2025}, organization = {IEEE}, doi = {10.1109/SoutheastCon56624.2025.10971676}, }
ISVLSI
Investigate the Effects of Laser Attack on Intelligence of the AV Perception

Abhijeet Solanki, S. R. Hasan, and T. N. Guo

In 2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) Mar 2024

Abs Bib

This paper investigates the effects of laser attacks on the intelligence of autonomous vehicle perception systems. Laser attacks can manipulate the data received by lidar sensors, leading to incorrect interpretations of the environment. We analyze the impact of such attacks on the performance of perception algorithms and propose mitigation strategies to enhance the robustness of these systems. The results demonstrate the vulnerability of current lidar-based perception systems to laser attacks and highlight the need for more resilient designs.
@inproceedings{solanki2024investigate, title = {Investigate the Effects of Laser Attack on Intelligence of the AV Perception}, author = {Solanki, Abhijeet and Hasan, S. R. and Guo, T. N.}, booktitle = {2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)}, pages = {779--782}, year = {2024}, organization = {IEEE}, doi = {10.1109/ISVLSI61997.2024.00152}, }
MWSCAS
Realistic GPS Spoofing via Customized CARLA GPS Navigation and Controller Systems

T. D. Robertson, W. Al Amiri, Abhijeet Solanki, S. R. Hasan, and T. Guo

In 2025 IEEE 68th International Midwest Symposium on Circuits and Systems (MWSCAS) Aug 2025

Abs Bib

This paper presents a method for realistic GPS spoofing using customized CARLA GPS navigation and controller systems. GPS spoofing is a significant threat to autonomous vehicles, as it can lead to incorrect positioning and navigation. We utilize the CARLA simulator to create a controlled environment for testing GPS spoofing techniques and evaluate their effectiveness in deceiving autonomous vehicle navigation systems. Our results demonstrate the potential risks associated with GPS spoofing and emphasize the importance of developing robust countermeasures to protect against such attacks.
@inproceedings{robertson2025realistic, title = {Realistic GPS Spoofing via Customized CARLA GPS Navigation and Controller Systems}, author = {Robertson, T. D. and Al Amiri, W. and Solanki, Abhijeet and Hasan, S. R. and Guo, T.}, booktitle = {2025 IEEE 68th International Midwest Symposium on Circuits and Systems (MWSCAS)}, pages = {720--724}, month = aug, year = {2025}, organization = {IEEE}, doi = {10.1109/MWSCAS53549.2025.11244479}, }
CBMS
Protecting Electronic Health Records in Transit and at Rest

Denis Ulybyshev, Christian Bare, Kristen Bellisario, Vadim Kholodilo, Bradley Northern, \textbfAbhijeet \textbfSolanki, and Timothy O’Donnell

In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) Aug 2020

Abs Bib 🏅Best Paper Award

Electronic Health Records (EHRs) are a critical component of modern healthcare systems, containing sensitive patient information that must be protected from unauthorized access and data breaches. In this paper, we propose a comprehensive approach to securing EHRs both in transit and at rest. Our method combines cryptographic techniques, access control mechanisms, and data watermarking to ensure the confidentiality and integrity of EHRs. We evaluate our approach using a simulated healthcare environment and demonstrate its effectiveness in preventing unauthorized access and mitigating the risks associated with data breaches. Our results highlight the importance of robust security measures for protecting EHRs and ensuring compliance with regulations such as HIPAA.
@inproceedings{9183319, author = {Ulybyshev, Denis and Bare, Christian and Bellisario, Kristen and Kholodilo, Vadim and Northern, Bradley and \textbf{Solanki}, \textbf{Abhijeet} and O'Donnell, Timothy}, booktitle = {2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)}, title = {Protecting Electronic Health Records in Transit and at Rest}, year = {2020}, volume = {}, number = {}, pages = {449-452}, keywords = {Cryptography;Access control;Servers;Containers;Electronic medical records;Metadata;Watermarking;Electronic Health Records, data privacy, data leakage prevention, HIPAA, access control}, doi = {10.1109/CBMS49503.2020.00091}, award_name = {Best Paper Award}, }