""" MAYA Self-Evolution v2 Autonomous self-improvement system for steganography detection. """ import json import math import base64 import hashlib import datetime import re import string import itertools import collections import dataclasses import html import urllib.parse import random from typing import Dict, List, Optional, Any, Union from dataclasses import dataclass, field @dataclass class EvolutionGenome: detection_weights: Dict[str, float] thresholds: Dict[str, float] feature_importance: Dict[str, float] generation: int = 0 fitness: float = 0.0 lineage: str = "" @dataclass class EvolutionState: generation: int population: List[EvolutionGenome] best_fitness: float avg_fitness: float diversity: float timestamp: str class MAYASelfEvolution: def __init__(self): self.state = EvolutionState( generation=0, population=[], best_fitness=0.0, avg_fitness=0.0, diversity=0.0, timestamp=datetime.datetime.now().isoformat() ) self.ancestors = [] self.performance_history = [] def initialize_population(self, size: int = 20) -> List[EvolutionGenome]: base_features = [ "lsb_variance", "alpha_entropy", "dct_coefficients", "histogram_analysis", "spatial_correlation", "bit_plane_analysis", "chi_square", "rs_analysis" ] population = [] for i in range(size): genome = EvolutionGenome( detection_weights={f: 1.0/len(base_features) for f in base_features}, thresholds={f: 0.5 for f in base_features}, feature_importance={f: 0.5 for f in base_features}, generation=0, fitness=0.0, lineage="root" ) for f in base_features: genome.detection_weights[f] = 0.1 + (hash(str(i)) % 100) / 200 genome.thresholds[f] = 0.3 + (hash(str(i*i)) % 70) / 100 genome.feature_importance[f] = 0.2 + (hash(str(i*3)) % 80) / 100 population.append(genome) self.state.population = population return population def calculate_fitness(self, genome: EvolutionGenome, test_results: Dict[str, float]) -> float: total_weight = sum(genome.detection_weights.values()) normalized_weights = {k: v/total_weight for k, v in genome.detection_weights.items()} score = 0.0 for feature, result in test_results.items(): if feature in genome.detection_weights: weight = normalized_weights.get(feature, 0.1) threshold = genome.thresholds.get(feature, 0.5) importance = genome.feature_importance.get(feature, 0.5) detection = 1.0 if result > threshold else result score += weight * detection * importance diversity_bonus = self._calculate_diversity_bonus(genome) genome.fitness = score + diversity_bonus return genome.fitness def _calculate_diversity_bonus(self, genome: EvolutionGenome) -> float: weights = list(genome.detection_weights.values()) mean_w = sum(weights) / len(weights) variance = sum((w - mean_w) ** 2 for w in weights) / len(weights) return min(variance * 10, 0.2) def mutate(self, genome: EvolutionGenome, rate: float = 0.1) -> EvolutionGenome: new_genome = EvolutionGenome( detection_weights=genome.detection_weights.copy(), thresholds=genome.thresholds.copy(), feature_importance=genome.feature_importance.copy(), generation=genome.generation + 1, fitness=0.0, lineage=genome.lineage + f"->G{genome.generation+1}" ) for feature in new_genome.detection_weights: if random.random() < rate: mutation = (random.random() - 0.5) * 0.2 new_genome.detection_weights[feature] = max(0.01, new_genome.detection_weights[feature] + mutation) if random.random() < rate * 0.5: mutation = (random.random() - 0.5) * 0.1 new_genome.thresholds[feature] = max(0.1, min(0.9, new_genome.thresholds[feature] + mutation)) if random.random() < rate * 0.3: mutation = (random.random() - 0.5) * 0.15 new_genome.feature_importance[feature] = max(0.1, min(1.0, new_genome.feature_importance[feature] + mutation)) return new_genome def crossover(self, parent1: EvolutionGenome, parent2: EvolutionGenome) -> EvolutionGenome: child = EvolutionGenome( detection_weights={}, thresholds={}, feature_importance={}, generation=max(parent1.generation, parent2.generation) + 1, fitness=0.0, lineage=f"{parent1.lineage}x{parent2.lineage}" ) for feature in parent1.detection_weights: if hash(feature) % 2 == 0: child.detection_weights[feature] = parent1.detection_weights[feature] child.thresholds[feature] = parent1.thresholds[feature] child.feature_importance[feature] = parent1.feature_importance[feature] else: child.detection_weights[feature] = parent2.detection_weights[feature] child.thresholds[feature] = parent2.thresholds[feature] child.feature_importance[feature] = parent2.feature_importance[feature] return child def evolve_generation(self, test_results: Dict[str, float], elite_size: int = 4) -> EvolutionState: if not self.state.population: self.initialize_population() fitness_scores = [] for genome in self.state.population: fitness = self.calculate_fitness(genome, test_results) fitness_scores.append((genome, fitness)) fitness_scores.sort(key=lambda x: x[1], reverse=True) elite = [g for g, _ in fitness_scores[:elite_size]] new_population = elite.copy() while len(new_population) < len(self.state.population): if len(new_population) < elite_size * 2: parent1, parent2 = elite[0], elite[1] else: parent1 = random.choice(elite) parent2 = random.choice(elite) if random.random() < 0.7: child = self.crossover(parent1, parent2) else: child = self.mutate(parent1) new_population.append(child) self.state.population = new_population[:len(self.state.population)] self.state.generation += 1 all_fitness = [g.fitness for g in self.state.population] self.state.best_fitness = max(all_fitness) self.state.avg_fitness = sum(all_fitness) / len(all_fitness) self.state.diversity = self._calculate_population_diversity() self.state.timestamp = datetime.datetime.now().isoformat() self.performance_history.append({ "generation": self.state.generation, "best_fitness": self.state.best_fitness, "avg_fitness": self.state.avg_fitness, "timestamp": self.state.timestamp }) return self.state def _calculate_population_diversity(self) -> float: if len(self.state.population) < 2: return 0.0 total_variance = 0.0 features = list(self.state.population[0].detection_weights.keys()) for feature in features: values = [g.detection_weights[feature] for g in self.state.population] mean = sum(values) / len(values) variance = sum((v - mean) ** 2 for v in values) / len(values) total_variance += variance return total_variance / len(features) def get_best_genome(self) -> Optional[EvolutionGenome]: if not self.state.population: return None return max(self.state.population, key=lambda g: g.fitness) def evolve_until_convergence(self, test_results: Dict[str, float], max_generations: int = 50, convergence_threshold: float = 0.001) -> EvolutionState: prev_fitness = 0.0 for gen in range(max_generations): self.evolve_generation(test_results) improvement = self.state.best_fitness - prev_fitness prev_fitness = self.state.best_fitness if improvement < convergence_threshold and gen > 10: break return self.state def export_state(self) -> Dict[str, Any]: best = self.get_best_genome() return { "generation": self.state.generation, "best_fitness": self.state.best_fitness, "avg_fitness": self.state.avg_fitness, "diversity": self.state.diversity, "timestamp": self.state.timestamp, "performance_history": self.performance_history, "population_size": len(self.state.population), "best_genome": self._genome_to_dict(best) if best else None } def _genome_to_dict(self, genome: EvolutionGenome) -> Dict[str, Any]: return { "detection_weights": genome.detection_weights, "thresholds": genome.thresholds, "feature_importance": genome.feature_importance, "generation": genome.generation, "fitness": genome.fitness, "lineage": genome.lineage } def create_maya_evolution_system(): """Initialize and return MAYA Self-Evolution system.""" mayan = MAYASelfEvolution() mayan.initialize_population(size=20) return mayan def handler(request): """ Server-side handler for MAYA Self-Evolution API. """ return { "message": "MAYA Self-Evolution v2 initialized", "system": "autonomous_self_improvement", "version": "2.0", "capabilities": [ "genetic_algorithm_evolution", "fitness_based_selection", "crossover_mutation", "diversity_preservation", "convergence_detection" ], "status": "ready", "timestamp": datetime.datetime.now().isoformat() } if __name__ == "__main__": mayan = create_maya_evolution_system() test_results = { "lsb_variance": 0.85, "alpha_entropy": 0.72, "dct_coefficients": 0.68, "histogram_analysis": 0.91, "spatial_correlation": 0.45, "bit_plane_analysis": 0.77, "chi_square": 0.83, "rs_analysis": 0.69 } print("=== MAYA Self-Evolution v2 ===") print(f"Initial population: {len(mayan.state.population)}") for gen in range(10): state = mayan.evolve_generation(test_results) print(f"Generation {state.generation}: Best={state.best_fitness:.4f}, Avg={state.avg_fitness:.4f}, Diversity={state.diversity:.4f}") best = mayan.get_best_genome() if best: print(f"\nBest genome fitness: {best.fitness:.4f}") print(f"Lineage: {best.lineage}") print("\nOptimized detection weights:") for feature, weight in sorted(best.detection_weights.items(), key=lambda x: x[1], reverse=True)[:5]: print(f" {feature}: {weight:.4f}") print("\n=== Evolution Complete ===") print(json.dumps(mayan.export_state(), indent=2))