# 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, []

# --- Example Usage ---
if __name__ == "__main__":
    engine = RealityIntegrityEngine()

    # Simulate analyzing a potentially deepfaked video
    result = engine.analyze_media("suspicious_video.mp4")
    print("\nAnalysis Result:")
    for key, value in result.items():
        print(f"  {key}: {value}")