AI-Enhancing Security with AI: Media Fabrication Detection Through Advanced Neural Architectures

Purpose: With the widespread adoption of AI-based content generation tools, the boundaries between authentic and synthetic media have blurred considerably. Fabricated video and image content — commonly referred to as deepfakes — now represent a tangible threat to individual privacy, electoral integrity, and institutional trust. This study addresses that challenge by proposing a detection framework capable of identifying forged visual media with high precision. Design/Methodology: A hybrid architecture is developed that unites the spatial discriminative power of Convolutional Neural Networks (CNNs) with the sequential reasoning capability of Long Short-Term Memory (LSTM) units. The CNN backbone — instantiated as a fine-tuned ResNeXt model — extracts rich frame-level feature representations, while the LSTM layer models temporal patterns across video frames. Findings: On the FaceForensics++ benchmark, the integrated architecture attains an overall detection accuracy of approximately 94%, surpassing both standalone CNN and standalone LSTM baselines by a clear margin. Explainability mechanisms are additionally incorporated to make predictions interpretable for end users and security analysts. Practical Implications: The system is designed for deployment in cybersecurity platforms, digital forensics workflows, and social media content moderation pipelines. Its modular design supports both cloud hosting and edge inference, making it suitable for diverse real-world environments.