💻 YouTube Deepfake Detection AI Implementation

See how YouTube's new AI tool identifies and labels AI-generated content programmatically.

import tensorflow as tf
from transformers import pipeline
import numpy as np

class YouTubeDeepfakeDetector:
    """
    YouTube's AI tool for detecting and labeling AI-generated content
    """
    
    def __init__(self):
        # Initialize with pre-trained models for video analysis
        self.video_analyzer = pipeline("video-classification", 
                                      model="facebook/timesformer-base-finetuned-k400")
        self.audio_detector = pipeline("audio-classification", 
                                      model="MIT/ast-finetuned-audioset-10-10-0.4593")
        
    def analyze_video(self, video_path):
        """
        Main method to analyze video for AI-generated content
        """
        # Extract frames and audio
        frames = self.extract_frames(video_path)
        audio = self.extract_audio(video_path)
        
        # Run detection algorithms
        video_ai_score = self.detect_ai_video(frames)
        audio_ai_score = self.detect_ai_audio(audio)
        
        # Combine scores for final decision
        combined_score = (video_ai_score * 0.7) + (audio_ai_score * 0.3)
        
        # Label if AI-generated
        if combined_score > 0.85:
            return {
                "is_ai_generated": True,
                "confidence": combined_score,
                "label": "AI-GENERATED CONTENT",
                "components": {
                    "video_ai": video_ai_score,
                    "audio_ai": audio_ai_score
                }
            }
        return {"is_ai_generated": False, "confidence": combined_score}
    
    def detect_ai_video(self, frames):
        """
        Detect AI-generated video patterns
        """
        # Analyze frame consistency and artifacts
        predictions = self.video_analyzer(frames[:10])  # Sample first 10 frames
        
        # Check for common AI generation patterns
        ai_patterns = ["unnatural_movement", "texture_inconsistency", 
                      "temporal_artifacts"]
        
        ai_score = np.mean([pred['score'] for pred in predictions 
                           if pred['label'] in ai_patterns])
        return ai_score
    
    def extract_frames(self, video_path):
        """
        Extract frames from video for analysis
        """
        # Frame extraction logic here
        return []
    
    def extract_audio(self, video_path):
        """
        Extract audio from video for analysis
        """
        # Audio extraction logic here
        return []