AI-based deepfake detection uses algorithms like CNNs and RNNs to spot anomalies in audio, video, or images—such as irregular lip-sync, eye movement, or lighting. As deepfakes grow more sophisticated, detection remains challenging, requiring constantly updated models, diverse datasets, and a hybrid approach combining AI with human verification to ensure accuracy.

Challenges in Detection
Deepfake technology is rapidly advancing, with models like StyleGAN3 and diffusion-based methods reducing detectable artifacts. Detection systems face issues like false positives from legitimate edits and false negatives from subtle fakes. Additionally, biased or limited training data can hinder accuracy across diverse faces, lighting, and resolutions.

The Enterprise Edition of ACE (Adaptive Cognito Engine) is a mobile-optimized AI platform that delivers real-time trust metrics using multimodal analysis of voice, emotion, and behavior to verify identity and detect deepfakes with adversarial robustness.

Real-World Example with Context

• Scenario: A bank receives a video call from someone claiming to be a CEO requesting a large fund transfer. The call is suspected to be a deepfake.
  
  

Detection Process:
The bank’s AI-driven fraud system analyzes videos using CNN to detect facial blending, RNN to spot irregular blinking, and audio-lip sync mismatches. With a 95% deepfake probability, a human analyst confirms the fraud, halting the transfer.