AI Scams in 2026: How To Identify Fake Voices, Videos & C...

#!/usr/bin/env python3
"""
AI Scam Detection Framework
Production-ready scam detection
"""

from typing import List, Dict
from dataclasses import dataclass
from enum import Enum

class ScamType(Enum):
    VOICE_CLONING = "voice_cloning"
    DEEPFAKE_VIDEO = "deepfake_video"
    AI_PHISHING = "ai_phishing"
    SOCIAL_ENGINEERING = "social_engineering"

@dataclass
class ScamDetection:
    detection_id: str
    scam_type: ScamType
    confidence: float
    indicators: List[str]

class AIScamDetector:
    """AI scam detection system."""
    
    def __init__(self):
        self.detections: List[ScamDetection] = []
    
    def detect_voice_cloning(self, audio_features: Dict) -> ScamDetection:
        """Detect voice cloning in audio."""
        indicators = []
        confidence = 0.0
        
        # Check for voice cloning indicators
        if audio_features.get('unnatural_pauses'):
            indicators.append("Unnatural pauses detected")
            confidence += 0.3
        
        if audio_features.get('robotic_quality'):
            indicators.append("Robotic voice quality")
            confidence += 0.4
        
        if audio_features.get('emotional_inconsistency'):
            indicators.append("Emotional inconsistency")
            confidence += 0.2
        
        detection = ScamDetection(
            detection_id=f"DET-{len(self.detections)+1}",
            scam_type=ScamType.VOICE_CLONING,
            confidence=min(confidence, 1.0),
            indicators=indicators
        )
        
        self.detections.append(detection)
        return detection
    
    def detect_deepfake_video(self, video_features: Dict) -> ScamDetection:
        """Detect deepfake video."""
        indicators = []
        confidence = 0.0
        
        # Check for deepfake indicators
        if video_features.get('lip_sync_issues'):
            indicators.append("Lip sync inconsistencies")
            confidence += 0.4
        
        if video_features.get('face_artifacts'):
            indicators.append("Facial artifacts detected")
            confidence += 0.5
        
        detection = ScamDetection(
            detection_id=f"DET-{len(self.detections)+1}",
            scam_type=ScamType.DEEPFAKE_VIDEO,
            confidence=min(confidence, 1.0),
            indicators=indicators
        )
        
        self.detections.append(detection)
        return detection

# Usage
detector = AIScamDetector()
voice_features = {
    'unnatural_pauses': True,
    'robotic_quality': True,
    'emotional_inconsistency': True
}
detection = detector.detect_voice_cloning(voice_features)
print(f"Scam detected: {detection.scam_type.value}, Confidence: {detection.confidence}")

#!/usr/bin/env python3
"""
Advanced AI Scam Detection System
Production-ready multi-modal scam detection with ML integration
"""

from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass, field, asdict
from enum import Enum
from datetime import datetime
import logging
import json
import numpy as np
from collections import defaultdict

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ScamType(Enum):
    """Scam type classification."""
    VOICE_CLONING = "voice_cloning"
    DEEPFAKE_VIDEO = "deepfake_video"
    AI_PHISHING = "ai_phishing"
    SOCIAL_ENGINEERING = "social_engineering"
    UNKNOWN = "unknown"

class RiskLevel(Enum):
    """Risk level classification."""
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"

@dataclass
class ScamDetection:
    """Comprehensive scam detection result."""
    detection_id: str
    scam_type: ScamType
    confidence: float
    risk_level: RiskLevel
    indicators: List[str]
    metadata: Dict = field(default_factory=dict)
    timestamp: datetime = field(default_factory=datetime.now)
    verified: bool = False
    
    def to_dict(self) -> Dict:
        """Convert to dictionary."""
        return {
            **asdict(self),
            'scam_type': self.scam_type.value,
            'risk_level': self.risk_level.value,
            'timestamp': self.timestamp.isoformat()
        }

class VoiceAnalyzer:
    """Analyzes audio for voice cloning indicators."""
    
    def analyze(self, audio_features: Dict) -> Dict:
        """Analyze audio features.
        
        Args:
            audio_features: Dictionary of audio features
            
        Returns:
            Analysis result dictionary
        """
        indicators = []
        confidence = 0.0
        
        # Check for unnatural pauses
        if audio_features.get('unnatural_pauses', False):
            indicators.append("Unnatural pauses detected")
            confidence += 0.3
        
        # Check for robotic quality
        if audio_features.get('robotic_quality', False):
            indicators.append("Robotic voice quality")
            confidence += 0.4
        
        # Check for emotional inconsistency
        if audio_features.get('emotional_inconsistency', False):
            indicators.append("Emotional inconsistency")
            confidence += 0.2
        
        # Check for background noise anomalies
        if audio_features.get('background_noise_anomaly', False):
            indicators.append("Background noise anomalies")
            confidence += 0.15
        
        # Check for frequency anomalies
        if audio_features.get('frequency_anomaly', False):
            indicators.append("Frequency spectrum anomalies")
            confidence += 0.25
        
        return {
            'indicators': indicators,
            'confidence': min(confidence, 1.0),
            'is_suspicious': confidence > 0.5
        }

class VideoAnalyzer:
    """Analyzes video for deepfake indicators."""
    
    def analyze(self, video_features: Dict) -> Dict:
        """Analyze video features.
        
        Args:
            video_features: Dictionary of video features
            
        Returns:
            Analysis result dictionary
        """
        indicators = []
        confidence = 0.0
        
        # Check for lip sync issues
        if video_features.get('lip_sync_issues', False):
            indicators.append("Lip sync inconsistencies")
            confidence += 0.4
        
        # Check for facial artifacts
        if video_features.get('face_artifacts', False):
            indicators.append("Facial artifacts detected")
            confidence += 0.5
        
        # Check for lighting inconsistencies
        if video_features.get('lighting_inconsistencies', False):
            indicators.append("Lighting inconsistencies")
            confidence += 0.3
        
        # Check for blurriness
        if video_features.get('unusual_blur', False):
            indicators.append("Unusual blur patterns")
            confidence += 0.2
        
        # Check for eye movement anomalies
        if video_features.get('eye_movement_anomaly', False):
            indicators.append("Eye movement anomalies")
            confidence += 0.35
        
        return {
            'indicators': indicators,
            'confidence': min(confidence, 1.0),
            'is_suspicious': confidence > 0.5
        }

class BehavioralAnalyzer:
    """Analyzes behavioral patterns for scam indicators."""
    
    SCAM_KEYWORDS = [
        'urgent', 'immediately', 'emergency', 'secret', 'don\'t tell',
        'wire transfer', 'gift cards', 'bitcoin', 'crypto', 'verify account',
        'suspended', 'locked', 'verify identity', 'confirm payment'
    ]
    
    def analyze(self, content: str, context: Dict) -> Dict:
        """Analyze behavioral patterns.
        
        Args:
            content: Text content to analyze
            context: Additional context (caller, timing, etc.)
            
        Returns:
            Analysis result dictionary
        """
        indicators = []
        confidence = 0.0
        
        content_lower = content.lower()
        
        # Check for scam keywords
        keyword_count = sum(1 for keyword in self.SCAM_KEYWORDS if keyword in content_lower)
        if keyword_count > 0:
            indicators.append(f"Found {keyword_count} scam-related keywords")
            confidence += min(keyword_count * 0.15, 0.6)
        
        # Check for urgency
        urgency_words = ['now', 'immediately', 'urgent', 'asap', 'right away']
        if any(word in content_lower for word in urgency_words):
            indicators.append("Urgency tactics detected")
            confidence += 0.3
        
        # Check for secrecy demands
        if 'don\'t tell' in content_lower or 'keep this secret' in content_lower:
            indicators.append("Secrecy demands detected")
            confidence += 0.4
        
        # Check for money requests
        money_indicators = ['send money', 'wire', 'transfer', 'payment', 'gift card']
        if any(indicator in content_lower for indicator in money_indicators):
            indicators.append("Money request detected")
            confidence += 0.5
        
        # Check timing (calls at unusual hours)
        if context.get('unusual_hour', False):
            indicators.append("Unusual call timing")
            confidence += 0.2
        
        return {
            'indicators': indicators,
            'confidence': min(confidence, 1.0),
            'is_suspicious': confidence > 0.5
        }

class AIScamDetector:
    """Production-ready AI scam detection system."""
    
    def __init__(self):
        self.detections: List[ScamDetection] = []
        self.voice_analyzer = VoiceAnalyzer()
        self.video_analyzer = VideoAnalyzer()
        self.behavioral_analyzer = BehavioralAnalyzer()
        self.detection_stats = defaultdict(int)
    
    def detect_voice_cloning(self, audio_features: Dict) -> ScamDetection:
        """Detect voice cloning in audio.
        
        Args:
            audio_features: Audio feature dictionary
            
        Returns:
            ScamDetection object
        """
        analysis = self.voice_analyzer.analyze(audio_features)
        
        confidence = analysis['confidence']
        risk_level = self._calculate_risk_level(confidence)
        
        detection = ScamDetection(
            detection_id=f"VOICE-{len(self.detections)+1}",
            scam_type=ScamType.VOICE_CLONING,
            confidence=confidence,
            risk_level=risk_level,
            indicators=analysis['indicators'],
            metadata={'audio_features': audio_features}
        )
        
        self.detections.append(detection)
        self.detection_stats['voice_cloning'] += 1
        
        logger.warning(f"Voice cloning detected: confidence={confidence:.2f}, risk={risk_level.value}")
        return detection
    
    def detect_deepfake_video(self, video_features: Dict) -> ScamDetection:
        """Detect deepfake video.
        
        Args:
            video_features: Video feature dictionary
            
        Returns:
            ScamDetection object
        """
        analysis = self.video_analyzer.analyze(video_features)
        
        confidence = analysis['confidence']
        risk_level = self._calculate_risk_level(confidence)
        
        detection = ScamDetection(
            detection_id=f"VIDEO-{len(self.detections)+1}",
            scam_type=ScamType.DEEPFAKE_VIDEO,
            confidence=confidence,
            risk_level=risk_level,
            indicators=analysis['indicators'],
            metadata={'video_features': video_features}
        )
        
        self.detections.append(detection)
        self.detection_stats['deepfake_video'] += 1
        
        logger.warning(f"Deepfake video detected: confidence={confidence:.2f}, risk={risk_level.value}")
        return detection
    
    def detect_behavioral_scam(self, content: str, context: Dict) -> ScamDetection:
        """Detect behavioral scam patterns.
        
        Args:
            content: Text content
            context: Additional context
            
        Returns:
            ScamDetection object
        """
        analysis = self.behavioral_analyzer.analyze(content, context)
        
        confidence = analysis['confidence']
        risk_level = self._calculate_risk_level(confidence)
        
        detection = ScamDetection(
            detection_id=f"BEHAVIOR-{len(self.detections)+1}",
            scam_type=ScamType.SOCIAL_ENGINEERING,
            confidence=confidence,
            risk_level=risk_level,
            indicators=analysis['indicators'],
            metadata={'content': content[:200], 'context': context}
        )
        
        self.detections.append(detection)
        self.detection_stats['behavioral'] += 1
        
        logger.warning(f"Behavioral scam detected: confidence={confidence:.2f}, risk={risk_level.value}")
        return detection
    
    def multi_modal_detect(self, audio_features: Optional[Dict] = None,
                          video_features: Optional[Dict] = None,
                          content: Optional[str] = None,
                          context: Optional[Dict] = None) -> List[ScamDetection]:
        """Perform multi-modal detection.
        
        Args:
            audio_features: Optional audio features
            video_features: Optional video features
            content: Optional text content
            context: Optional context
            
        Returns:
            List of detections
        """
        detections = []
        
        if audio_features:
            detections.append(self.detect_voice_cloning(audio_features))
        
        if video_features:
            detections.append(self.detect_deepfake_video(video_features))
        
        if content:
            detections.append(self.detect_behavioral_scam(content, context or {}))
        
        return detections
    
    def _calculate_risk_level(self, confidence: float) -> RiskLevel:
        """Calculate risk level from confidence.
        
        Args:
            confidence: Detection confidence (0-1)
            
        Returns:
            RiskLevel enum
        """
        if confidence >= 0.8:
            return RiskLevel.CRITICAL
        elif confidence >= 0.6:
            return RiskLevel.HIGH
        elif confidence >= 0.4:
            return RiskLevel.MEDIUM
        else:
            return RiskLevel.LOW
    
    def get_statistics(self) -> Dict:
        """Get detection statistics.
        
        Returns:
            Statistics dictionary
        """
        return {
            'total_detections': len(self.detections),
            'by_type': dict(self.detection_stats),
            'by_risk_level': {
                level.value: len([d for d in self.detections if d.risk_level == level])
                for level in RiskLevel
            },
            'high_risk_count': len([d for d in self.detections if d.risk_level in [RiskLevel.HIGH, RiskLevel.CRITICAL]])
        }
    
    def export_detections(self, format: str = "json") -> str:
        """Export detections.
        
        Args:
            format: Export format (json)
            
        Returns:
            Exported detections string
        """
        detections_dict = [d.to_dict() for d in self.detections]
        return json.dumps(detections_dict, indent=2)
    
    def cleanup(self):
        """Clean up resources."""
        logger.info("Cleaning up AI scam detector resources")
        # In production, close connections, save state, etc.


# Example usage
if __name__ == "__main__":
    detector = AIScamDetector()
    
    # Voice cloning detection
    voice_features = {
        'unnatural_pauses': True,
        'robotic_quality': True,
        'emotional_inconsistency': True,
        'background_noise_anomaly': True
    }
    voice_detection = detector.detect_voice_cloning(voice_features)
    print(f"Voice detection: {voice_detection.risk_level.value}, Confidence: {voice_detection.confidence:.2f}")
    
    # Deepfake video detection
    video_features = {
        'lip_sync_issues': True,
        'face_artifacts': True,
        'lighting_inconsistencies': True
    }
    video_detection = detector.detect_deepfake_video(video_features)
    print(f"Video detection: {video_detection.risk_level.value}, Confidence: {video_detection.confidence:.2f}")
    
    # Behavioral detection
    content = "This is urgent! I need you to send $5000 immediately via wire transfer. Don't tell anyone!"
    context = {'unusual_hour': True}
    behavioral_detection = detector.detect_behavioral_scam(content, context)
    print(f"Behavioral detection: {behavioral_detection.risk_level.value}, Confidence: {behavioral_detection.confidence:.2f}")
    
    # Multi-modal detection
    all_detections = detector.multi_modal_detect(
        audio_features=voice_features,
        video_features=video_features,
        content=content,
        context=context
    )
    print(f"\nTotal detections: {len(all_detections)}")
    
    # Statistics
    stats = detector.get_statistics()
    print(f"\nStatistics: {json.dumps(stats, indent=2)}")

#!/usr/bin/env python3
"""
Unit tests for AI Scam Detector
"""

import pytest
from ai_scam_detector import (
    AIScamDetector, ScamType, RiskLevel,
    VoiceAnalyzer, VideoAnalyzer, BehavioralAnalyzer
)

class TestAIScamDetector:
    """Tests for AIScamDetector."""
    
    @pytest.fixture
    def detector(self):
        return AIScamDetector()
    
    def test_voice_cloning_detection(self, detector):
        """Test voice cloning detection."""
        features = {
            'unnatural_pauses': True,
            'robotic_quality': True
        }
        detection = detector.detect_voice_cloning(features)
        
        assert detection.scam_type == ScamType.VOICE_CLONING
        assert detection.confidence > 0
        assert len(detection.indicators) > 0
    
    def test_deepfake_video_detection(self, detector):
        """Test deepfake video detection."""
        features = {
            'lip_sync_issues': True,
            'face_artifacts': True
        }
        detection = detector.detect_deepfake_video(features)
        
        assert detection.scam_type == ScamType.DEEPFAKE_VIDEO
        assert detection.confidence > 0
    
    def test_behavioral_detection(self, detector):
        """Test behavioral scam detection."""
        content = "Urgent! Send money immediately!"
        detection = detector.detect_behavioral_scam(content, {})
        
        assert detection.scam_type == ScamType.SOCIAL_ENGINEERING
        assert detection.confidence > 0
    
    def test_multi_modal_detection(self, detector):
        """Test multi-modal detection."""
        detections = detector.multi_modal_detect(
            audio_features={'robotic_quality': True},
            content="Send money now!"
        )
        
        assert len(detections) >= 2
    
    def test_statistics(self, detector):
        """Test statistics generation."""
        detector.detect_voice_cloning({'robotic_quality': True})
        stats = detector.get_statistics()
        
        assert stats['total_detections'] >= 1

if __name__ == "__main__":
    pytest.main([__file__, "-v"])

#!/usr/bin/env python3
"""
AI Scam Detector Cleanup
Production-ready cleanup and resource management
"""

import logging
from datetime import datetime, timedelta

logger = logging.getLogger(__name__)

class AIScamDetectorCleanup:
    """Handles cleanup operations for AI scam detector."""
    
    def __init__(self, detector):
        """Initialize cleanup handler.
        
        Args:
            detector: AIScamDetector instance
        """
        self.detector = detector
    
    def cleanup_old_detections(self, days: int = 30):
        """Remove detections older than specified days.
        
        Args:
            days: Number of days to keep detections
        """
        cutoff_date = datetime.now() - timedelta(days=days)
        initial_count = len(self.detector.detections)
        
        self.detector.detections = [
            d for d in self.detector.detections
            if d.timestamp >= cutoff_date
        ]
        
        removed = initial_count - len(self.detector.detections)
        logger.info(f"Cleaned up {removed} old detections")
        return removed
    
    def cleanup(self):
        """Perform complete cleanup."""
        logger.info("Starting AI scam detector cleanup")
        
        # Clean up old detections
        self.cleanup_old_detections()
        
        # Clean up detector resources
        self.detector.cleanup()
        
        logger.info("AI scam detector cleanup complete")