Leveraging Machine Learning and AI for Deepfake Recognition

Computer science

The emergence of deepfake videos has introduced a significant challenge to digital security, media integrity, and public trust. These artificially manipulated videos, powered by deep learning technologies, that are increasingly being used to spread misinformation, conduct cybercrimes, and manipulate public opinion. Conventional detection mechanisms largely rely on computationally intensive video or audio analysis, such as identifying facial artifacts, speech irregularities, or frame inconsistencies. However, these methods are resource-heavy, limited in scalability, and often ineffective when video quality is compromised or when adversaries employ obfuscation techniques. This research proposes a metadata-driven detection framework leverages the machine learning algorithms trained on structured attributes including video identifiers, titles, tags, publishing times, engagement metrics, and textual descriptions to classify videos as genuine or manipulated. Numerous classifiers, Support Vector Machine (SVM), Logistic Regression, Gradient Boosting, Decision Tree, K-Nearest Neighbours, are assessed based on accuracy, precision, recall, F1-score, with optimization through preprocessing and hyperparameter tuning. The system is deployed via a Flask-based web-application, offering real-time predictions through a user-friendly interface. By shifting from resource-heavy video analysis to contextual metadata evaluation, the proposed system provides a lightweight, and practical solution for combating the rising threat of deepfake media.

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