Deepfake Detection in the Era of Multimedia: Methods, Gaps, and Evolving Research Directions

The aloft complexity of deepfake technology has sparked serious concerns across domains including journalism, cybersecurity, political discourse, and digital identity. Fueled by advancements in deep learning, synthetic media can now convincingly mimic human expressions, voice patterns, and behaviours, challenging the boundaries of trust in multimedia content. This paper provides a comprehensive investigation into state-of-the-art detection methods across video, audio, and multimodal formats. By categorizing leading approaches—including convolutional networks, spectrogram-based analysis, and cross-modal consistency frameworks—we expose technical limitations in scalability, generalization, and explainability. Additionally, we highlight gaps in ethical governance and the absence of cross-industry standards to regulate deepfake mitigation. The study advocates for evolving detection strategies rooted in adversarial robustness, multimodal fusion, and privacy-aware learning. Through this interdisciplinary lens, we chart a roadmap for the next generation of deepfake detection systems capable of safeguarding digital authenticity without compromising civil liberties. The insights presented herein aim to empower researchers, policymakers, and platform developers to co-create resilient, future-ready defences against synthetic manipulation.