Citation :

Anish Chelliserikatil Pavithran, "Preventing the Impact of AI Deepfakes on Journalistic Integrity: A Hybrid Approach to Detection and Prevention," International Journal of Computer Science and Engineering, vol. 13, no. 8, pp. 1-7, 2026. Crossref, https://doi.org/10.14445/23488387/IJCSE-V13I8P101

Abstract

Deepfakes pose a direct challenge to journalistic verification because convincing synthetic video and audio can circulate faster than manual fact-checking, while many high-performing detectors remain costly or lose reliability under compression and domain shift. This study presents a newsroom-oriented hybrid framework that combines lightweight visual screening, temporal consistency analysis, selective frequency-domain verification, optional audio analysis, content-provenance checks, and human editorial review. MobileNetV3 provides low-cost frame triage, a shallow GRU/LSTM models short-term temporal inconsistencies, and the frequency-aware stage is reserved for ambiguous content to limit computational overhead. Available branch scores are combined through late fusion, and uncertain or high-risk items are escalated to editors. The reported model comparison yields 91.8% accuracy, 89.4% precision, 90.5% recall, and an 89.9% F1-score for the MobileNet-LSTM configuration, exceeding the three internal baselines reported in this study. The contribution is not a claim of superior benchmark accuracy over all state-of-the-art detectors; rather, it is an operational design that integrates efficient detection, provenance evidence, multimodal checks, and editorial judgment for resource-constrained newsroom workflows.

Keywords

Content Provenance, Deepfake Detection, Hybrid Human-Ai Systems, Journalistic Integrity, MobileNetV3.

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