Deepfake Detection Using AI: A Review Of Audio, Visual And Audio–Visual Methods For Digital Forensics

Deepfakes are realistic manipulated images, videos and audio created with deep learning and generative models that can imitate a person's face, voice, expressions or movements. Although useful in entertainment and education, they are misused for misinformation, fake identities, fraud and reputational harm. This paper reviews AI-based deepfake detection across both visual and audio modalities. It explains generation methods, then examines visual detection from facial texture, lighting, eye and lip movement and inter-frame differences using convolutional and attention-based networks; generalizable detectors for fully synthetic images; audio detection from spectral and prosodic voice characteristics using anti-spoofing models; and audio–visual methods that detect mismatches between speech and lip motion. Benchmarks including FaceForensics++, ASVspoof and FakeAVCeleb are discussed. The detection process of collecting and preprocessing media, extracting features and applying machine learning or deep learning models is formalized in a five-layer architecture. Research gaps, objectives, a comparison of modalities and challenges such as generalization, compression, adversarial evasion and explainability for forensic use are presented. The review concludes that combining audio and visual evidence gives the most reliable detection for social media, journalism, banking, law enforcement and digital forensics.