# Conceptual Architecture for a RealityIntegrityEngine
class RealityIntegrityEngine:
def __init__(self, model_path="pre_trained_physics_model.h5"):
"""Initializes the engine with trained physical anomaly detection models."""
self.visual_analyzer = VisualPhysicsAnalyzer()
self.audio_analyzer = AudioPhysicsAnalyzer()
self.cross_modal_correlator = CrossModalIntegrator()
self.anomaly_detector_ai = AnomalyDetectorAI(model_path)
def analyze_media(self, media_path: str) -> dict:
"""
Processes a media file (video/audio) to detect deepfake anomalies
at the physical layer.
"""
# Step 1: Ingest and Decompose Media
video_frames, audio_samples = self._ingest_and_preprocess(media_path)
# Step 2: Extract Physical Features
# Focus on "minute inconsistencies in skin blood flow, unique light reflections in the cornea"
visual_features = self.visual_analyzer.extract_features(video_frames)
# Focus on "imperceptible tremor patterns in synthetic voices"
audio_features = self.audio_analyzer.extract_features(audio_samples)
# Step 3: Analyze Cross-Modal Consistency
# Look for subtle discrepancies between visual and auditory physics
cross_modal_features = self.cross_modal_correlator.correlate(
visual_features['sync_points'], audio_features['sync_points']
)
# Step 4: Aggregate Features for AI Anomaly Detection
# Combine all extracted physical feature vectors
combined_features = {
**visual_features,
**audio_features,
**cross_modal_features
}
# Step 5: Predict Authenticity using a pre-trained AI
authenticity_score, detailed_anomalies = self.anomaly_detector_ai.predict(combined_features)
return {
"authenticity_score": authenticity_score,
"anomalies_found": detailed_anomalies,
"status": "Authentic" if authenticity_score > 0.7 else "Deepfake Detected"
}
def _ingest_and_preprocess(self, media_path: str):
"""Placeholder for media ingestion and initial processing."""
print(f"Ingesting and preprocessing: {media_path}")
# In a real system, this would extract video frames, audio waveforms, etc.
return {"frame_data": []}, {"audio_data": []}
# --- Core Modules for Physical Feature Extraction ---
class VisualPhysicsAnalyzer:
def extract_features(self, video_frames: list) -> dict:
"""Analyzes video frames for physical inconsistencies."""
print("Analyzing visual physics: skin blood flow, corneal reflections...")
skin_blood_flow_anomalies = self._detect_skin_blood_flow(video_frames)
corneal_reflection_anomalies = self._analyze_corneal_reflections(video_frames)
return {
"skin_blood_flow": skin_blood_flow_anomalies,
"corneal_reflections": corneal_reflection_anomalies,
"sync_points": self._get_visual_sync_points(video_frames)
}
def _detect_skin_blood_flow(self, frames: list) -> list:
"""Detects minute inconsistencies in skin blood flow patterns over time."""
# Sophisticated signal processing on pixel values to detect pulsatile changes.
return ["lack of natural pulsatility in cheek (frame 120-150)"]
def _analyze_corneal_reflections(self, frames: list) -> list:
"""Examines unique light reflections in the cornea for physical realism."""
# Advanced image processing to check for consistent specular highlights,
# distortion patterns indicative of a spherical, fluid surface.
return ["simplified or inconsistent corneal highlights (frame 88)"]
class AudioPhysicsAnalyzer:
def extract_features(self, audio_samples: list) -> dict:
"""Analyzes audio samples for physical inconsistencies."""
print("Analyzing audio physics: voice tremor patterns...")
voice_tremor_anomalies = self._analyze_voice_tremors(audio_samples)
return {
"voice_tremors": voice_tremor_anomalies,
"sync_points": self._get_audio_sync_points(audio_samples)
}
def _analyze_voice_tremors(self, samples: list) -> list:
"""Detects imperceptible tremor patterns in synthetic voices."""
# Spectral analysis to identify unnatural periodicity or lack of complexity in micro-variations of pitch and amplitude.
return ["unnatural regularity in fundamental frequency (segment 0:34-0:38)"]
class CrossModalIntegrator:
def correlate(self, visual_sync: list, audio_sync: list) -> dict:
"""Correlates visual and audio physical cues for consistency."""
print("Integrating cross-modal physics for consistency...")
# Beyond simple lip-sync: checking for subtle physical reactions (e.g., throat muscle movement correlating with voice onset).
return {"cross_modal_discrepancies": ["slight desynchronization in throat muscle activation and vocal onset (0:15)"]}
class AnomalyDetectorAI:
def __init__(self, model_path: str):
"""Loads a pre-trained AI model for physical anomaly detection."""
print(f"Loading AI anomaly detection model from {model_path}...")
self.model = {"loaded_model": True} # Placeholder for a real deep learning model
def predict(self, features: dict) -> tuple:
"""Uses the AI model to predict authenticity based on physical features."""
print("Running AI prediction for authenticity...")
# This model would likely be trained on massive datasets of real and deepfaked media,
# learning the complex distribution of 'real' physics and flagging deviations.
# It could be an autoencoder, a classifier, or a more complex adversarial network.
# Simple heuristic for demonstration:
total_anomalies = sum(len(v) for k, v in features.items() if isinstance(v, list))
if total_anomalies > 2:
return 0.2, ["Multiple physical inconsistencies detected."]
return 0.95, []</spa