DEEPFAKE DETECTION USING ADVANCED IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES FOR CYBERSECURITY

Source abstract

Deepfake generation technologies based on generative adversarial networks, autoencoders, and diffusion models have advanced to the point where synthetic media can closely mimic authentic visual content, posing significant threats to cybersecurity through misinformation, impersonation, and fraud. A mathematically principled deepfake detection framework is proposed, combining frequency-domain statistical features, convolutional representation learning, and a calibrated decision-theoretic classifier to deliver robust and interpretable detection. The methodology incorporates risk-aware threshold selection derived from expected cost minimization, enabling a balance between minimizing false negatives and controlling false positives, critical for operational cybersecurity environments. Experimental evaluation on a curated dataset demonstrates accuracy exceeding 90%, precision ≈0.92, recall ≈0.90, and area under the ROC curve of approximately 0.95, validating both discriminative power and generalization. Precision–recall analysis further reveals consistent high-precision operation until very high recall thresholds, confirming the model’s stability. Grad-CAM style interpretability maps provide visual localization of manipulated regions, strengthening analyst confidence in automated outputs. The proposed framework is computationally efficient, scalable, and readily integrable into digital forensics pipelines, social media moderation systems, and identity verification workflows. Future research directions include adversarial training for robustness, incorporation of temporal and physiological features for video-based deepfake detection, and the addition of abstention mechanisms to handle out-of-distribution or adversarially perturbed inputs, thereby enhancing resilience against evolving synthetic media threats.