Browser fingerprinting has emerged as a powerful mechanism for device identification, user tracking, fraud detection, and cybersecurity analytics. Recent advances in deep learning have significantly improved the predictive accuracy of browser fingerprinting systems by enabling automated extraction of complex behavioral and configuration-based features. However, increasing dependence on deep neural networks introduces a critical security challenge: adversarial vulnerability. Small, carefully crafted perturbations in browser attributes can manipulate model predictions, thereby undermining system reliability and trustworthiness. This paper proposes a comprehensive framework for evaluating adversarial robustness in deep learning-based browser fingerprinting systems. The framework integrates feature-level analysis, adversarial threat modeling, robustness assessment metrics, attack simulation procedures, interpretability mechanisms, and resilience evaluation criteria. A research-driven synthesis of existing deep learning literature, optimization techniques, and adversarial learning concepts is employed to establish a multidimensional evaluation methodology. The framework provides systematic guidance for assessing model behavior under adversarial conditions while maintaining practical relevance for privacy protection, fraud prevention, and digital identity management. The study highlights critical robustness factors, identifies current research gaps, and proposes future directions for developing secure and explainable browser fingerprinting systems.

Browser Fingerprinting, Deep Learning, Adversarial Robustness, Cybersecurity

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