aceA Comparative Study of Deep Learning Techniques for Deepfake Detection
Deepfake media generated or altered using artificial intelligence can imitate real people and events with increasing visual realism, creating challenges for digital trust, privacy, cybersecurity, journalism, and forensic investigation. This paper presents a comparative literature-based study of deep learning techniques used for deepfake detection. Representative approaches are examined across spatial, temporal, frequency-aware, transformer-based, and hybrid categories. Major public datasets, including FaceForens…
Different deep learning families each contribute complementary detection strengths: CNNs extract spatial features efficiently, temporal models catch frame-to-frame inconsistencies, and frequency-aware methods expose forensic traces.
Detector performance measured only within the training distribution can overstate practical reliability, while realistic AI-made fakes threaten digital trust, privacy, cybersecurity, journalism, and forensic work.
The study is literature-based and notes that results measured only within the training distribution can overstate practical reliability, so reported detector performance may not generalize to real-world deepfakes.
Evidence
- Peer-reviewedInternational Journal of Advanced Research in Science Communication and Technology2026-10-17
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Truvace Impact Record TRV-2026-1860, v1: “A Comparative Study of Deep Learning Techniques for Deepfake Detection.” Truvace, 2026-10-18. /record/TRV-2026-1860 (accessed at citation time). sha256 9239021f778a040e…
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