Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness

Deepfakes (DFs) have emerged as a significant threat in recent years, being exploited for malicious purposes such as impersonation, misinformation dissemination, and artistic style imitation, thereby raising critical ethical and security concerns. This survey presents a comprehensive analysis of passive DF detection methods across image, video, and audio modalities, addressing critical gaps in existing literature. Unlike previous surveys which examine modalities in isolation, we explore inter-modality relationships and shared challenges. We systematically categorize detection approaches based on their underlying methodologies: forensic-based, data-driven, fingerprint-based, and hybrid techniques for visual modalities, and handcrafted versus learnable features for audio. We also extend our analysis beyond mere detection accuracy to include essential performance dimensions for real-world deployment, including generalization and robustness. Additionally, this survey provides an in-depth empirical evaluations of 50 state-of-the-art detection methods across 10 popular datasets, assessing their performance in three critical dimensions: (1) within-domain accuracy, (2) cross-domain generalization, and (3) robustness against adversarial attacks. Our experiments reveal a persistent generalization gap, with performance degradations of 15-20% in cross-domain scenarios, and vulnerability to white-box adversarial attacks exceeding 80% success rates. We also analyze the advantages and limitations of existing datasets, benchmarks, and evaluation metrics for passive DF detection. Finally, we propose future research directions to address these unexplored and emerging issues in the field of passive DF detection. This survey serves as a comprehensive resource for researchers and practitioners, providing insights into the current landscape, methodological approaches, and promising future directions in this rapidly evolving field.