Real-Time Detection of AI-Generated Deepfakes and Phishing Cartography Using Multimodal Deep Learning
- Aug 17, 2023
- 1 min read
Original Research | 2026 | Volume 1 | Issue 2 | Page 58-64
Dr. Akhilesh Kumar, ORCID ID, Chief Technology Officer, Department of Information Technology, Santosh Deemed to be University, Ghaziabad, Uttar Pradesh 201009, Email Id: Linuz.akhilesh@gmail.com
Abstract
The rapid proliferation of sophisticated AI-generated deepfakes and increasingly complex phishing schemes poses a critical threat to digital security, institutional integrity, and public trust. Traditional detection methods often struggle to keep pace with the evolving nature of these adversarial threats, which leverage advanced generative models and social engineering tactics. This research proposes a novel, robust framework for the real-time detection of deepfakes and the mapping of phishing cartography utilizing a multimodal deep learning approach. By integrating heterogeneous data sources—including visual, acoustic, and behavioral telemetry—our model achieves high-fidelity identification of synthetic media and malicious phishing infrastructure. The proposed architecture employs a cross-modal feature fusion technique that enhances the discriminative capacity of the classifier, enabling the detection of subtle artifacts often overlooked by unimodal systems. Furthermore, we develop a dynamic cartographic module to visualize and categorize phishing propagation patterns, providing actionable intelligence for real-time mitigation. Experimental results demonstrate significant improvements in detection accuracy, reduced latency, and enhanced resilience against adversarial attacks compared to state-of-the-art baselines. This study contributes to the development of proactive defensive mechanisms essential for securing information ecosystems in an era of automated deception.
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