005_具身人工智能的感知安全挑战：防御传感器欺骗与对抗攻击

# 2025年多模态融合安全框架示例（基于PyTorch）
import torch
import torch.nn as nn

class SecureMultimodalFusion(nn.Module):
    def __init__(self, camera_dim, lidar_dim, imu_dim, output_dim):
        super().__init__()
        # 模态特征提取器
        self.camera_encoder = nn.Sequential(...)
        self.lidar_encoder = nn.Sequential(...)
        self.imu_encoder = nn.Sequential(...)
        
        # 异常检测模块
        self.anomaly_detector = AnomalyDetectionModule(...)
        
        # 安全融合层
        self.fusion_layer = SecureAttentionFusion(...)
        
        # 输出层
        self.output_layer = nn.Linear(..., output_dim)
    
    def forward(self, camera_data, lidar_data, imu_data):
        # 提取特征
        camera_feat = self.camera_encoder(camera_data)
        lidar_feat = self.lidar_encoder(lidar_data)
        imu_feat = self.imu_encoder(imu_data)
        
        # 检测异常
        camera_anomaly = self.anomaly_detector(camera_feat, "camera")
        lidar_anomaly = self.anomaly_detector(lidar_feat, "lidar")
        imu_anomaly = self.anomaly_detector(imu_feat, "imu")
        
        # 安全融合 - 动态调整异常模态的权重
        weights = self._calculate_security_weights([camera_anomaly, lidar_anomaly, imu_anomaly])
        fused_feat = self.fusion_layer([camera_feat, lidar_feat, imu_feat], weights)
        
        # 输出结果
        return self.output_layer(fused_feat)

# 2025年先进的传感器异常检测系统
class AdvancedSensorAnomalyDetector:
    def __init__(self, config):
        self.config = config
        # 初始化各模态异常检测模块
        self.camera_detector = self._init_camera_detector()
        self.lidar_detector = self._init_lidar_detector()
        self.imu_detector = self._init_imu_detector()
        
        # 跨模态相关性验证器
        self.correlation_validator = CrossModalCorrelationValidator()
        
        # 异常历史记录和趋势分析
        self.anomaly_history = {}
        self.trend_analyzer = TrendAnalyzer()
    
    def _init_camera_detector(self):
        # 初始化摄像头异常检测模块
        return CameraAnomalyDetector(
            reconstruction_model=self.config['camera']['reconstruction_model'],
            confidence_threshold=self.config['camera']['confidence_threshold']
        )
    
    def _init_lidar_detector(self):
        # 初始化LiDAR异常检测模块
        return LidarAnomalyDetector(
            point_density_threshold=self.config['lidar']['point_density_threshold'],
            temporal_consistency_window=self.config['lidar']['temporal_consistency_window']
        )
    
    def _init_imu_detector(self):
        # 初始化IMU异常检测模块
        return IMUAnomalyDetector(
            statistical_model=self.config['imu']['statistical_model'],
            physical_constraints=self.config['imu']['physical_constraints']
        )
    
    def detect_anomalies(self, sensor_data, timestamp):
        """检测所有传感器数据中的异常"""
        anomalies = {}
        
        # 检测单模态异常
        if 'camera' in sensor_data:
            camera_anomaly = self.camera_detector.detect(sensor_data['camera'], timestamp)
            if camera_anomaly['is_anomalous']:
                anomalies['camera'] = camera_anomaly
        
        if 'lidar' in sensor_data:
            lidar_anomaly = self.lidar_detector.detect(sensor_data['lidar'], timestamp)
            if lidar_anomaly['is_anomalous']:
                anomalies['lidar'] = lidar_anomaly
        
        if 'imu' in sensor_data:
            imu_anomaly = self.imu_detector.detect(sensor_data['imu'], timestamp)
            if imu_anomaly['is_anomalous']:
                anomalies['imu'] = imu_anomaly
        
        # 跨模态相关性验证
        correlation_anomalies = self.correlation_validator.validate(
            sensor_data, anomalies, timestamp
        )
        anomalies.update(correlation_anomalies)
        
        # 更新历史记录并分析趋势
        self._update_history(anomalies, timestamp)
        trend_anomalies = self.trend_analyzer.analyze(self.anomaly_history, timestamp)
        anomalies.update(trend_anomalies)
        
        # 生成防御建议
        defense_recommendations = self._generate_defense_recommendations(anomalies)
        
        return {
            'timestamp': timestamp,
            'anomalies': anomalies,
            'defense_recommendations': defense_recommendations,
            'confidence_score': self._calculate_confidence_score(anomalies)
        }
    
    def _update_history(self, anomalies, timestamp):
        # 更新异常历史记录
        for sensor_type, anomaly in anomalies.items():
            if sensor_type not in self.anomaly_history:
                self.anomaly_history[sensor_type] = []
            self.anomaly_history[sensor_type].append({
                'timestamp': timestamp,
                'anomaly_type': anomaly['type'],
                'severity': anomaly['severity']
            })
    
    def _generate_defense_recommendations(self, anomalies):
        # 根据检测到的异常生成防御建议
        recommendations = []
        
        # 高优先级防御：多模态失效
        if len(anomalies) >= 2:
            recommendations.append({
                'priority': 'high',
                'action': 'activate_fallback_sensors',
                'description': '多个传感器模态同时失效，启动备用传感器系统'
            })
        
        # 针对特定传感器的防御建议
        if 'camera' in anomalies:
            recommendations.append({
                'priority': 'medium',
                'action': 'increase_lidar_weight',
                'description': '摄像头异常，增加LiDAR和IMU数据权重'
            })
        
        # 物理攻击特征检测
        if any(a.get('attack_type') == 'physical' for a in anomalies.values()):
            recommendations.append({
                'priority': 'high',
                'action': 'physical_inspection_alert',
                'description': '检测到物理攻击特征，建议进行物理检查'
            })
        
        return recommendations
    
    def _calculate_confidence_score(self, anomalies):
        # 计算异常检测的置信度分数
        confidence = 1.0
        for anomaly in anomalies.values():
            confidence *= (1 - anomaly['confidence'])
        return 1 - confidence

# 跨模态相关性验证器
class CrossModalCorrelationValidator:
    def validate(self, sensor_data, current_anomalies, timestamp):
        """验证不同传感器模态之间的相关性"""
        correlation_anomalies = {}
        
        # 验证摄像头和LiDAR数据一致性
        if 'camera' in sensor_data and 'lidar' in sensor_data:
            if self._validate_camera_lidar_correlation(
                sensor_data['camera'], 
                sensor_data['lidar']
            ) < self.config['correlation_threshold']:
                correlation_anomalies['camera_lidar_correlation'] = {
                    'is_anomalous': True,
                    'type': 'correlation_inconsistency',
                    'severity': 'high',
                    'confidence': 0.95,
                    'description': '摄像头和LiDAR数据存在严重不一致'
                }
        
        # 验证IMU和视觉运动一致性
        if 'camera' in sensor_data and 'imu' in sensor_data:
            if self._validate_motion_consistency(
                sensor_data['camera'], 
                sensor_data['imu']
            ) < self.config['motion_consistency_threshold']:
                correlation_anomalies['motion_consistency'] = {
                    'is_anomalous': True,
                    'type': 'motion_inconsistency',
                    'severity': 'medium',
                    'confidence': 0.88,
                    'description': '视觉运动估计与IMU测量不一致'
                }
        
        return correlation_anomalies

# 物理不变性感知网络示例
class PhysicallyInvariantPerceptionNetwork(nn.Module):
    def __init__(self, base_model, augmentation_config):
        super().__init__()
        self.base_model = base_model
        self.augmentation_config = augmentation_config
        
        # 物理变换模拟器
        self.physical_transformer = PhysicalEnvironmentSimulator(
            lighting_conditions=self.augmentation_config['lighting'],
            viewing_angles=self.augmentation_config['angles'],
            distances=self.augmentation_config['distances'],
            weather_effects=self.augmentation_config['weather']
        )
        
        # 鲁棒性正则化器
        self.robustness_regularizer = AdversarialRobustnessRegularizer()
    
    def forward(self, x, is_training=False):
        if is_training:
            # 训练模式：应用物理变换增强鲁棒性
            transformed_x = self._apply_physical_transforms(x)
            
            # 多视角预测
            outputs = []
            for x_t in transformed_x:
                outputs.append(self.base_model(x_t))
            
            # 集成预测
            ensemble_output = self._ensemble_predictions(outputs)
            
            return ensemble_output
        else:
            # 推理模式：直接前向传播
            return self.base_model(x)
    
    def _apply_physical_transforms(self, x):
        """应用多样化的物理变换模拟真实环境变化"""
        transformed_samples = []
        
        # 应用不同光照条件
        for lighting in self.augmentation_config['lighting']:
            transformed = self.physical_transformer.apply_lighting(x, lighting)
            transformed_samples.append(transformed)
        
        # 应用不同视角
        for angle in self.augmentation_config['angles']:
            transformed = self.physical_transformer.apply_rotation(x, angle)
            transformed_samples.append(transformed)
        
        # 应用不同距离
        for distance in self.augmentation_config['distances']:
            transformed = self.physical_transformer.apply_scaling(x, distance)
            transformed_samples.append(transformed)
        
        return transformed_samples
    
    def _ensemble_predictions(self, predictions):
        """集成多个变换下的预测结果"""
        # 简单平均集成
        return torch.mean(torch.stack(predictions), dim=0)
    
    def train_step(self, x, y, optimizer):
        """增强的训练步骤，包含鲁棒性正则化"""
        self.train()
        optimizer.zero_grad()
        
        # 标准损失
        pred = self.forward(x, is_training=True)
        standard_loss = nn.CrossEntropyLoss()(pred, y)
        
        # 鲁棒性正则化损失
        robust_loss = self.robustness_regularizer.compute(self, x, y)
        
        # 组合损失
        total_loss = standard_loss + 0.3 * robust_loss
        
        total_loss.backward()
        optimizer.step()
        
        return {
            'loss': total_loss.item(),
            'standard_loss': standard_loss.item(),
            'robust_loss': robust_loss.item()
        }