AI Deep Trace RealAI Deep Trace Real-Time Video Deepfake Detection in War Zones: Case Study – Sudan War(Khart oum)

Abstract: This study introduces an AI-powered Deep Trace system for real-time detection of deepfake videos during the Sudan War (2023–2025), particularly the Battle of Khartoum. Using Convolutional Neural Networks (CNNs) and Multimodal fusion, Deep Trace identifies manipulated content spread through social media and messaging platforms under conflict conditions. Verified fact-checks revealed that several viral videos claiming Forgener airstrikes were actually filmed at Khartoum International Airport. Simulations achieved an AUC of 0.91 and accuracy of 91% under low-bitrate video compression, demonstrating DeepTrace’s potential to mitigate misinformation and enhance situational awareness in active war zones. Deepfake technology AI-generated synthetic video and audio has become a critical threat to information integrity, especially within volatile conflict environments. Adversaries can impersonate political leaders, military officials, or civilians in real-time streams, enabling propaganda, fraud, and psychological warfare. This paper presents a real-time deepfake detection framework inspired by DeepTrace/Sensity (Deeptrace Labs, 2019; DeepTrace AI, 2025), integrating multimodal AI analysis and deployment-ready engineering. The system combines visual forensic analysis, audio liveness and voice-clone detection (Federal Trade Commission [FTC], 2023), and challenge–response verification tests (Hegde, Mittal, & Memon, 2024), supported by an instant alerting mechanism. Algorithms and pipelines are optimized for low-latency operation to function effectively under conflict conditions. Experimental evaluations on benchmark datasets (Celeb-DF, DFDC) and simulated war-zone scenarios demonstrate strong detection performance, with an AUC ≈ 0.88 and alerting latency ranging from sub-second to a few seconds. The system proves practical for use in command centers, newsrooms, and digital platforms that require immediate authentication of media content. Real-time deepfake detection thus represents a necessary defense layer against AI-driven disinformation in war zones, though adversarial evasion, domain shift, and governance challenges remain Keywords: Deepfake detection; War zones; Sudan War; Real-time AI; Multimedia forensics; DeepTrace; Audio liveness; Video forensics; Information warfare.