Overview
Background
I am a passionate researcher with a background in computer science and a strong commitment to leveraging technology for the betterment of society. I hold a PhD in Image Forensics and have had the privilege of conducting postdoctoral research at prestigious institutions such as SUNY Albany and Dartmouth College, where I had the opportunity to collaborate closely with Prof. Hany Farid.
During my postdoc at Dartmouth College, I focused my research efforts on addressing a critical societal issue - real-time child pornography detection. This research not only garnered recognition within the academic community but also earned praise from luminaries like Prof. Ramesh Raskar at MIT, who invited me to share my insights through a talk at MIT.
I primarily works in the area of Cyber Security, Digital Forensics, Privacy and Security Aspects, Homomorphic Encryption and Cloud Computing.
As I continue my research journey, I remain committed to making a positive impact through innovation and collaboration. I am excited about the opportunities that lie ahead and the potential for technology to create a safer and more inclusive world.
Availability
- Dr Priyanka Singh is:
- Available for supervision
Fields of research
Qualifications
- Bachelor of Information Technology, Uttar Pradesh Technical University
- Masters (Research) of Image Processing, Motilal Nehru National Institute of Technology
- Doctor of Philosophy of Image Processing, Motilal Nehru National Institute of Technology
Research interests
-
Towards Mitigation of AI Based Attacks
The adoption of Artificial Intelligence (AI) has made a paradigmatic shift in the cybersecurity challenges. On the positive side, AI enables faster detection of anomalies, automated responses to attacks, and predictive threat modeling. This helps organisations to proactively combat cyber threats and minimise the impact of attacks. On the contrary, the integration of AI also opens new avenues for adversaries. Cybercriminals are increasingly leveraging AI to craft sophisticated attacks, such as automated phishing schemes, deepfake technology, and AI-driven malware that can adapt to traditional defence mechanisms. The existing cybersecurity frameworks lack adequate update to address this sophistication.
-
Facilitating confidential and effective Cyber Threat Intelligence sharing
Cyber Threat Intelligence (CTI) data is shared 24/7 between industry organisations and government agencies to provide incident responders real-time information about attackers' tactics and origins. Theoretically, this collaborative approach uplifts sectors’ cyber resilience but in reality, CTI sharing is often done inefficiently and ineffectively due to organisations' reticence to share full information which may unknowingly reveal organisational data (e.g., to competing or unknown entities within the community). This project aims to urgently address these challenges through a secure, collaborative CTI sharing approach with robust privacy protection built for AI-driven cybersecurity systems facilitating swift sector-wide incident response.
-
Security and Privacy of Large Language Models
As Large Language Models (LLMs) become increasingly pervasive in applications ranging from natural language understanding to creative content generation, the need for robust privacy protections becomes more critical. These models, which are trained on vast amounts of data, raise significant concerns regarding data security, user privacy, and ethical implications. This project aims to explore the privacy challenges inherent in LLMs, addressing both technical and legal dimensions while highlighting best practices for privacy-preserving AI development.
-
Combat Propagation of Dis/Misinformation on Social Media
Disinformation refers to deliberate misleading of people by manipulating narrative or facts. The unprecedented propagation of disinformation on social media can influence one’s mindset and cause an adverse impact on the society. AI-enabled tracking of the entire pipeline of disinformation by identifying the source, the target audience, the hidden agenda, the media of propagation, and the kind of influence can potentially combat this problem by reverse-identification of the origin of disinformation narratives and helping authorities to address the falsification problem.
-
From Prediction to Explanation: Multimodal, Explainable, and Interactive Deepfake Detection Framework for Non-Expert Users
The proliferation of deepfake technologies poses urgent challenges and serious risks to digital integrity, particularly within critical sectors such as forensics, journalism, and the legal system. While existing detection systems have made significant progress in classification accuracy, they typically function as black-box models-offering limited transparency and minimal support for human reasoning. This lack of interpretability hinders their usability in real-world decision-making contexts, especially for non-expert users.
Research impacts
My work focusses on using cutting-edge technology to tackle real-world challenges, particularly in the realm of online safety and security. I am driven by the belief that responsible and ethical technology can play a transformative role in protecting the most vulnerable in our society.
Works
Search Professor Priyanka Singh’s works on UQ eSpace
2025
Conference Publication
Continual contrastive learning on tabular data with out of distribution
Ginanjar, Achmad, Li, Xue, Singh, Priyanka and Hua, Wen (2025). Continual contrastive learning on tabular data with out of distribution. European Symposium on Artificial Neural Networks 2025, Bruges, Belgium, 23-25 April 2025. Louvain-la-Neuve, Belgium: Ciaco. doi: 10.14428/esann/2025.es2025-141
2024
Conference Publication
Towards Explainable Network Intrusion Detection using Large Language Models
Houssel, Paul R. B., Singh, Priyanka, Layeghy, Siamak and Portmann, Marius (2024). Towards Explainable Network Intrusion Detection using Large Language Models. 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Sharjah, United Arab Emirates, 16-19 December 2024. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/bdcat63179.2024.00021
2024
Conference Publication
An evaluation of enigma machine online emulators for teaching and learning
Utami, Dyah Puji, Singh, Priyanka and Manoharan, Sathiamoorthy (2024). An evaluation of enigma machine online emulators for teaching and learning. 2024 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), Bengaluru, India, 9-12 December 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/tale62452.2024.10834342
2024
Conference Publication
Unsupervised learning for insider threat prediction: a behavioral analysis approach
Mehmood, Rahat, Singh, Priyanka and Jeffery, Zoe (2024). Unsupervised learning for insider threat prediction: a behavioral analysis approach. 2024 17th International Conference on Security of Information and Networks (SIN), Sydney, Australia, 2-4 December 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/sin63213.2024.10871807
2024
Conference Publication
FakeFaceDiscriminator: discrimination of AI-synthesized fake faces
Liu, Xufeng, Vu, Quoc Hoan and Singh, Priyanka (2024). FakeFaceDiscriminator: discrimination of AI-synthesized fake faces. 2024 17th International Conference on Security of Information and Networks (SIN), Sydney, Australia, 2-4 December 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/sin63213.2024.10871737
2024
Conference Publication
IRIS-SAFE: privacy-preserving biometric authentication
Listiyani, Devi and Singh, Priyanka (2024). IRIS-SAFE: privacy-preserving biometric authentication. 2024 17th International Conference on Security of Information and Networks (SIN), Sydney, Australia, 2-4 December 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/sin63213.2024.10871371
2024
Conference Publication
CDCEF: a cloud-based data volatility & CSP reliance eradication framework
Sharma, Pankaj Hariom and Singh, Priyanka (2024). CDCEF: a cloud-based data volatility & CSP reliance eradication framework. 2024 17th International Conference on Security of Information and Networks (SIN), Sydney, Australia, 2-4 December 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/sin63213.2024.10871550
2024
Conference Publication
Exploiting correlation between facial action units for detecting deepfake videos
Vu, Quoc Hoan and Singh, Priyanka (2024). Exploiting correlation between facial action units for detecting deepfake videos. 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, United States, 7-9 August 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/mipr62202.2024.00106
2024
Conference Publication
Guarding against ChatGPT threats: identifying and addressing vulnerabilities
Zhang, Dingzong, Jain, Khushi and Singh, Priyanka (2024). Guarding against ChatGPT threats: identifying and addressing vulnerabilities. 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, United States, 7-9 August 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/mipr62202.2024.00104
2024
Journal Article
PP-PRNU: PRNU-based source camera attribution with privacy-preserving applications
Jena, Riyanka, Singh, Priyanka, Mohanty, Manoranjan and Das, Manik Lal (2024). PP-PRNU: PRNU-based source camera attribution with privacy-preserving applications. Computing, 106 (10), 3309-3333. doi: 10.1007/s00607-024-01330-w
2024
Conference Publication
SADHE: secure anomaly detection for GPS trajectory based on homomorphic encryption
Singh, Priyanka, Rathi, Jash and Patel, Priyankaben Babulal (2024). SADHE: secure anomaly detection for GPS trajectory based on homomorphic encryption. 39th Annual ACM Symposium on Applied Computing (SAC), Avila, Spain, 8-12 April 2024. New York, NY, United States: ACM. doi: 10.1145/3605098.3636165
2024
Conference Publication
Privacy-preserving disease prediction with secure data deduplication on untrusted cloud servers
Jain, Khushi, Singh, Priyanka and Li, Xue (2024). Privacy-preserving disease prediction with secure data deduplication on untrusted cloud servers. 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR), San Jose, CA, United States, 7 - 9 August 2024. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/MIPR62202.2024.00114
2023
Conference Publication
RAFT: evaluating federated learning resilience against threats
Kumar, Mayank, Agrawal, Radha and Singh, Priyanka (2023). RAFT: evaluating federated learning resilience against threats. 2023 16th International Conference on Security of Information and Networks (SIN), Jaipur, India, 20-21 November 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/sin60469.2023.10474931
2023
Conference Publication
BATFL: battling backdoor attacks in federated learning
Kumar, Mayank, Agrawal, Radha and Singh, Priyanka (2023). BATFL: battling backdoor attacks in federated learning. 2023 16th International Conference on Security of Information and Networks (SIN), Jaipur, India, 20-21 November 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/sin60469.2023.10474981
2023
Journal Article
PP-JPEG: A Privacy-Preserving JPEG Image-Tampering Localization
Jena, Riyanka, Singh, Priyanka and Mohanty, Manoranjan (2023). PP-JPEG: A Privacy-Preserving JPEG Image-Tampering Localization. Journal of Imaging, 9 (9) 172, 1-15. doi: 10.3390/jimaging9090172
2023
Conference Publication
PPAuth: A privacy-preserving framework for authentication of digital image
Jena, Riyanka, Singh, Priyanka and Mohanty, Manoranjan (2023). PPAuth: A privacy-preserving framework for authentication of digital image. 7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023, Be'er Sheva, Israel, 29–30 June 2023. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-34671-2_14
2023
Conference Publication
Data poisoning attack by label flipping on SplitFed learning
Gajbhiye, Saurabh, Singh, Priyanka and Gupta, Shaifu (2023). Data poisoning attack by label flipping on SplitFed learning. 5th International Conference, RTIP2R 2022, Kingsville, TX United States, 1-2 December 2022. Cham, Switzerland: Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-031-23599-3_30
2023
Book Chapter
XAIForCOVID-19: A comparative analysis of various explainable AI techniques for COVID-19 diagnosis using chest x-ray images
Patel, Nisarg, Parmar, Siddhraj, Singh, Priyanka and Mohanty, Manoranjan (2023). XAIForCOVID-19: A comparative analysis of various explainable AI techniques for COVID-19 diagnosis using chest x-ray images. Communications in computer and information science. (pp. 503-517) edited by Deep Gupta, Kishor Bhurchandi, Subrahmanyam Murala, Balasubramanian Raman and Sanjeev Kumar. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-31417-9_38
2022
Journal Article
An image forensic technique based on JPEG ghosts
Singh, Divakar, Singh, Priyanka, Jena, Riyanka and Chakraborty, Rajat Subhra (2022). An image forensic technique based on JPEG ghosts. Multimedia Tools and Applications, 82 (9), 14153-14169. doi: 10.1007/s11042-022-13699-x
2022
Conference Publication
SHELBRS: Location-based recommendation services using switchable homomorphic encryption
Jain, Mishel, Singh, Priyanka and Raman, Balasubramanian (2022). SHELBRS: Location-based recommendation services using switchable homomorphic encryption. 11th International Conference on Security, Privacy, and Applied Cryptography Engineering (SPACE), Kolkata, India, 10–13 December 2021. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-95085-9_4
Supervision
Availability
- Dr Priyanka Singh is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Available projects
-
Research projects
-
Investigating vulnerabilities of Split Learning/Federated Learning/Split-Fed Learning Models
-
Defenses to build Robust Deep Learning Models
-
Image/Audio/Video Forensic Techniques to Detect Doctored Images/Deep Fakes
-
Privacy Preserving Frameworks for Various Applications
-
Geo-hashing and Location Based Services
-
Supervision history
Current supervision
-
Doctor Philosophy
Privacy preservation with Large Language Models
Principal Advisor
Other advisors: Professor Xue Li
-
Doctor Philosophy
Understanding Privacy Concerns About Living in Smart Cities and Enhancing Privacy Awareness
Principal Advisor
Other advisors: Professor Xue Li
-
Master Philosophy
Multi-modal Detection of Video Disinformation
Principal Advisor
Other advisors: Professor Xue Li
-
Doctor Philosophy
Advancing cyberworthiness via integration of AI and Model-Based Systems Engineering (MBSE)
Principal Advisor
Other advisors: Professor Ryan Ko
-
Doctor Philosophy
Weighted Ensembles for Different machine learning model that support non-data-sharing / vertical partition
Associate Advisor
Other advisors: Professor Xue Li
-
Doctor Philosophy
Identification of Disinformation Origins with Evidence-Based Justification
Associate Advisor
Other advisors: Professor Xue Li
-
Doctor Philosophy
Behaviour-Oriented Tracking of Disinformation
Associate Advisor
Other advisors: Professor Xue Li
-
Master Philosophy
Mapping Emerging Narratives Composed of False Information
Associate Advisor
Other advisors: Professor Xue Li
Media
Enquiries
For media enquiries about Dr Priyanka Singh's areas of expertise, story ideas and help finding experts, contact our Media team: