A Novel Hybrid Framework for Deepfake Detection

Fast developments in artificial intelligence technology produced out- standing generative advancements that produced highly realistic deepfake media content which consistently outpaces existing method detection capabilities. The rising distribution of synthetic content leads to urgent threats for media authen- ticity because privacy and security issues remain even though detection systems need to be implemented immediately. The proposed combination of lightweight deepfake detection model solves present problems related to spatial-temporal fea- ture analysis and adaptive adversarial noise reduction and noise-resilient feature extraction methods.The Xception backbone operates with temporal attention to find inconsistencies between compressed video frames through the Celeb-DF-v1 and Celeb-DF-v2 datasets at real-time speeds for inference. Deepfake video de- tection on Celeb-DF-v2 achieved a top-tier success rate of 90% accuracy thus surpassing all current competing solutions by 5.7%. At the same time the pro- posed model maintained strong effectiveness when facing actual video defor- mation challenges and different dataset environments. The model shows great efficiency in combination with adaptability making it ideal for social media en- vironments where it defends against evolving synthetic media dangers at scale. Supported by future enhancement research we consider the limitations for im- proving attacks detection on previously unobserved adversary conditions.