Ai-Based Deepfake Detection: A Review Of Spatial Artifact, Temporal Consistency And Biological Signal Approaches For Real-Time Media Verification

Abstract

Deepfakes, realistic manipulations of faces, voices and videos produced with generative adversarial networks and diffusion models, threaten digital media integrity, personal privacy and public security. Traditional verification by visual inspection or simple forensic tools struggles against these sophisticated alterations. This paper reviews AI-based deepfake detection with emphasis on approaches suited to a real-time framework. It explains how deepfakes are generated and then surveys three families of detection cues: spatial artifacts within individual frames, such as warping, blending boundaries and texture inconsistencies captured by convolutional networks; temporal inconsistencies across frames, including unnatural blinking and lip motion detected with recurrent and spatio-temporal models; and biological signals such as subtle colour changes caused by blood flow that synthetic faces fail to reproduce. Benchmark datasets including FaceForensics++, Celeb-DF and the DeepFake Detection Challenge are discussed. A five-layer real-time detection architecture covering media ingestion, face processing, multi-cue analysis, decision fusion and reporting is proposed, together with research gaps, objectives, a comparison of methods and challenges such as generalization to unseen generators, compression and adversarial attacks. The review concludes that fusing complementary cues offers the most reliable automated defence.