Browser fingerprinting has emerged as a critical technique for user identification, fraud detection, access control, and cybersecurity monitoring in environments where traditional authentication mechanisms may be insufficient. However, modern browser fingerprinting systems face significant challenges arising from adversarial manipulation, privacy-preserving browser technologies, and the opaque nature of deep learning models. While deep neural architectures have demonstrated substantial improvements in identification accuracy, their susceptibility to adversarial attacks and lack of interpretability undermine trust, reliability, and deployment readiness. This study proposes an Explainable and Adversarially Resilient Deep Learning Framework for Secure Browser Fingerprinting and User Identification that integrates robust feature representation, adversarial defense mechanisms, and explainable artificial intelligence (XAI) principles. The framework combines feature extraction, adversarial training, resilience evaluation, and explain ability modules to improve identification reliability under hostile conditions. Through a research and review-based methodological approach, the study synthesizes recent developments in artificial intelligence robustness, foundation model architectures, sociotechnical AI considerations, and ethical deployment practices. The findings indicate that explain ability and adversarial resilience should be treated as complementary objectives rather than independent system characteristics. The proposed framework enhances transparency, security, and operational trustworthiness while addressing emerging threats against browser fingerprinting systems. The study contributes a conceptual foundation for future secure digital identity infrastructures that require both predictive performance and interpretability.

Browser Fingerprinting, User Identification, Explainable Artificial Intelligence, Adversarial Robustness

AJ Alvero, Courtney Peña, "AI Sentience and Socioculture", Journal of Social Computing, vol.4, no.3, pp.205-220, 2023.

Amanda Potasznik, "Press Release Ethics in AI: Performative Ethics in for-Profit AI Companies", 2025 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), pp.1-10, 2025.

Chen Zhao, Xingyuan Dai, Yisheng Lv, Yonglin Tian, Yuhai Ren, Fei-Yue Wang, "Foundation Models for Transportation Intelligence: ITS Convergence in TransVerse", IEEE Intelligent Systems, vol.37, no.6, pp.77-82, 2022.

Chen Zhao, Xiao Wang, Yisheng Lv, Yonglin Tian, Yilun Lin, Fei-Yue Wang, "Parallel Transportation in TransVerse: From Foundation Models to De CAST", IEEE Transactions on Intelligent Transportation Systems, vol.24, no.12, pp.15310-15327, 2023.

Ganapathy, S. K. (2025). Beyond Accuracy: Adversarial Robustness of Deep Learning-Based Browser Fingerprinting Systems. Frontiers in Emerging Artificial Intelligence and Machine Learning, 2(12), 29–39. https://doi.org/10.64917/feaiml/Volume02Issue12-03