Evaluating Gabor, Latent, and Fused Features for Zero-Shot DeepfakeDetection with Isolation Forest and OCSVM.A comparative Study

The prevalence of deepfake technology has led to increased risks to biometric security, social media integrity, and the useof audio and video content for disinformation. In response, studies have progressed to provide efficient detecting techniques.Specifically, this paper uses anomaly-based classifiers to give a comparative analysis of zero-shot learning-based deepfakedetection. We evaluate two classifiers: One-Class Support Vector Machine (OCSVM) and Isolation Forest (IF) with threedifferent feature settings: Gabor features, Latent features, and Fused features (a mix of Gabor and Latent features). Importantmeasures like F1 Score, Accuracy, Precision, and Recall are used to assess how well the classifiers perform. Our resultsprovide important insights and future directions into the relationship between feature types and classifier performance in thesetting of zero-shot learning.