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| import asyncio | |
| import logging | |
| import random | |
| from datetime import UTC, datetime | |
| from typing import Any | |
| logger = logging.getLogger(__name__) | |
| class FederatedLearningPrototype: | |
| """ | |
| Federated Forensic Intelligence. | |
| Allows local knowledge exchange without data sharing. | |
| Ref: ZENITH_VISION | |
| """ | |
| def __init__(self): | |
| self.drift_threshold = 0.05 | |
| self.local_participation_rate = 0.85 | |
| async def synchronize_weights(self) -> dict[str, Any]: | |
| """ | |
| Synchronizes local fraud model weights with the federated mesh. | |
| Simulates weight aggregation and differential privacy application. | |
| """ | |
| logger.info("Synchronizing federated model weights with mesh") | |
| # Simulate active peers in the mesh | |
| active_peers = random.randint(8, 24) | |
| sync_drift = random.uniform(0.001, 0.04) | |
| return { | |
| "status": "SYNCED", | |
| "global_loss": 0.015 + sync_drift, | |
| "peers_engaged": active_peers, | |
| "privacy_method": "Differential Privacy (Epsilon=0.1)", | |
| "sync_quality": "High" if sync_drift < self.drift_threshold else "Degraded", | |
| "last_sync": datetime.now(UTC).isoformat(), | |
| } | |
| class AdversarialForensicShield: | |
| """ | |
| Deepfake & Synthetic Evidence Detection. | |
| Protects the integrity of visual/audio forensic artifacts. | |
| Ref: ZENITH_VISION | |
| """ | |
| def __init__(self): | |
| self.detection_models = ["GAN-Spatial-Consistency", "Temporal-Drift-Analysis"] | |
| async def verify_artifact(self, artifact_id: str) -> dict[str, Any]: | |
| """ | |
| Detects adversarial perturbations or synthetic generation using multi-model voting. | |
| """ | |
| logger.info(f"Scanning artifact {artifact_id} for adversarial signals") | |
| # Simulate neural scanning | |
| await asyncio.sleep(0.5) | |
| # Randomly simulate a potential detection for a "suspicious" ID | |
| is_suspicious = "suspicious" in artifact_id.lower() | |
| synthetic_prob = ( | |
| random.uniform(0.4, 0.9) if is_suspicious else random.uniform(0.001, 0.01) | |
| ) | |
| return { | |
| "artifact_id": artifact_id, | |
| "synthetic_probability": round(synthetic_prob, 4), | |
| "adversarial_perturbation_detected": synthetic_prob > 0.5, | |
| "source_device_fingerprint": ( | |
| "MATCHED (Canon EOS 5D)" if not is_suspicious else "SIGNATURE_ABSENT" | |
| ), | |
| "integrity_score": round(1.0 - synthetic_prob, 4), | |
| "models_consulted": self.detection_models, | |
| } | |
| class AutonomousHuntingAgent: | |
| """ | |
| Autonomous Forensic Hunting Agent. | |
| Self-healing hypotheses and proactive fraud discovery. | |
| Ref: ZENITH_VISION | |
| """ | |
| def __init__(self): | |
| self.active_hypotheses = [] | |
| async def run_discovery_cycle(self) -> list[dict[str, Any]]: | |
| """ | |
| Runs an autonomous hunt for undiscovered patterns using emergent behavior analysis. | |
| """ | |
| logger.info("Autonomous agent running discovery cycle") | |
| findings = [] | |
| # Simulate 1-2 emergent findings | |
| num_findings = random.randint(1, 2) | |
| discovery_types = [ | |
| ("Temporal Structuring", "Emergent circular fund flow in dormant accounts"), | |
| ( | |
| "Cross-Jurisdictional Ghosting", | |
| "Rapid asset fragmentation across offshore nodes", | |
| ), | |
| ( | |
| "Identity Synthesis", | |
| "Algorithmic generation of synthetic counterparties", | |
| ), | |
| ] | |
| for _ in range(num_findings): | |
| dtype, finding = random.choice(discovery_types) | |
| findings.append( | |
| { | |
| "hypothesis_id": f"auto_h_{random.randint(100, 999)}", | |
| "finding_type": dtype, | |
| "finding": finding, | |
| "confidence": round(random.uniform(0.75, 0.92), 2), | |
| "action_taken": "ISOLATED_AND_FLAGGED", | |
| "reasoning": "Pattern deviates from baseline transaction entropy.", | |
| } | |
| ) | |
| return findings | |
| def get_zenith_horizon(): | |
| return { | |
| "federated": FederatedLearningPrototype(), | |
| "adversarial": AdversarialForensicShield(), | |
| "autonomous": AutonomousHuntingAgent(), | |
| } | |