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Fraud Prevention Today,
Billions Saved Tomorrow

Adaptive Cognito Engine (ACE) is a mobile-optimized AI platform for real-time inference, integrating multimodal models for data extraction and adversarial robustness.

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AI Model Illustration

Advanced AI Breakthroughs In Video

Trust Factor Engine (TFE)

Faceoff’s engine uses 8 models with dynamic weighting to score trust (1–10), offering nuanced insights beyond binary deepfake detection.

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Deepfake Detection

Faceoff is Multi-AI Fusion Architecture, where 8 AI models perform cross-modal trust analysis, outperforming single-modality systems.

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FaceOff Lite

Optimized for real-time identity verification, liveness checks, and continuous authentication on edge devices or live camera feeds.

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Federated FaceOff

The concept where individuals are rightful owners of their data is known as Data Ownership or Data Sovereignty.

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Facepay

FaceOff’s AI-powered behavioral biometrics ensures real-time, secure fraud prevention across PayTM, BharatPe, GPay, UPI 123Pay, NEFT, and RTGS.

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Synthetic Fraud

Faceoff uses privacy-safe, multimodal AI to detect deepfakes and synthetic fraud across finance, insurance, law, and digital media.

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FaceOff Deepfake Image Detection

FaceOff provides an AI-powered deepfake image detection platform that accurately identifies manipulated or AI-generated facial images using advanced computer vision and forensic intelligence. Built for low latency, enterprise scale, and privacy-by-design, it helps banks, enterprises, platforms, and governments prevent identity fraud, impersonation, and digital misinformation.

Seamless Onboarding, Intelligent Feedback & Continuous Learning

FaceOff Onboarding enables fast, secure access to its Deepfake Image Detection platform. Users can upload images or integrate APIs to receive real-time authenticity scores and forensic insights. A built-in feedback loop powers the Adaptive Cognito Engine, enabling continuous online learning so the system evolves against new deepfake techniques with industry-leading accuracy.

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Deepfake Detection: Combating AI's Evolving Threat

Deepfakes are increasingly hyper-realistic, demanding advanced detection strategies. Future defenses rely on multimodal analysis, behavioral biometrics, and Oxygen Concentric-level content authentication. Combining real-time detection, public awareness, legal frameworks, and industry collaboration with GenAI technologies like generative adversarial networks and multimodal sensing algorithms is vital to safeguarding digital trust against sophisticated AI-driven misinformation.

Deepfakes surged 245% in 2023, with over 500,000 video/voice fakes globally, heavily used to manipulate public opinion during elections.
Synthetic identity fraud, amplified by deepfakes, is a growing global threat, affecting 49% of US and 39% of Singaporean businesses.
The Take It Down Act (2025) combats non-consensual deepfake porn, enabling victims to request removal and addressing 73% lacking prior legal recourse.
Gartner forecasts that by 2026, 30% of online video content will be synthetic, with limited reliable detection capabilities.
Forrester reports a surge in deepfake fraud targeting banking and insurance, urging accelerated investment in advanced AI-driven detection solutions.
IDC stresses the rising need for content provenance and real-time verification tools to effectively combat increasingly sophisticated digital threats.

Trust Factor Detection In Video Interviews

Upload any video interview, and our cutting-edge AI-powered truth detection
engine will instantly analyze each statement—revealing what’s genuine and what’s not.

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