fallbacks.py

import torch
import torch.nn as nn
import torch.nn.functional as F

# Compact fallback implementations for optional components.
# These are intentionally simple and used only when full implementations
# or external dependencies are unavailable.

class RemotePhysiologicalAnalyzer(nn.Module):
    """Basic physiological analyzer as fallback."""
    def __init__(self, feature_dim=32):
        super(RemotePhysiologicalAnalyzer, self).__init__()
        self.feature_dim = feature_dim

    def forward(self, frames):
        batch_size = frames.shape[0]
        device = frames.device
        return {
            'naturalness': torch.ones(batch_size, 1, device=device) * 0.5
        }

class OculomotorDynamicsAnalyzer(nn.Module):
    """Analyzes eye movement dynamics (fallback)."""
    def __init__(self, hidden_dim=64):
        super(OculomotorDynamicsAnalyzer, self).__init__()
        self.hidden_dim = hidden_dim

    def forward(self, eye_landmarks):
        batch_size = eye_landmarks.shape[0]
        device = eye_landmarks.device
        naturalness = torch.ones(batch_size, 1, device=device) * 0.5
        dynamics = torch.zeros(batch_size, self.hidden_dim, device=device)
        return naturalness, dynamics

class LightingConsistencyAnalyzer(nn.Module):
    """Analyzes lighting consistency across frames (fallback)."""
    def __init__(self, feature_dim=64):
        super(LightingConsistencyAnalyzer, self).__init__()
        self.feature_dim = feature_dim

    def forward(self, frames):
        batch_size = frames.shape[0]
        device = frames.device
        return torch.ones(batch_size, 1, device=device) * 0.5

class TextureAnalyzer(nn.Module):
    """Analyzes texture patterns for deepfake artifacts (fallback)."""
    def __init__(self, patch_size=32, feature_dim=64):
        super(TextureAnalyzer, self).__init__()
        self.patch_size = patch_size
        self.feature_dim = feature_dim

    def forward(self, frames):
        batch_size = frames.shape[0]
        device = frames.device
        consistency = torch.ones(batch_size, 1, device=device) * 0.5
        features = torch.zeros(batch_size, self.feature_dim, device=device)
        return consistency, features

class FrequencyDomainAnalyzer(nn.Module):
    """Analyzes frequency domain artifacts (fallback)."""
    def __init__(self, feature_dim=64):
        super(FrequencyDomainAnalyzer, self).__init__()
        self.feature_dim = feature_dim

    def forward(self, frames):
        batch_size = frames.shape[0]
        device = frames.device
        return torch.ones(batch_size, 1, device=device) * 0.5

class GANFingerprintDetector(nn.Module):
    """Detects GAN fingerprints in images (fallback)."""
    def __init__(self, feature_dim=128):
        super(GANFingerprintDetector, self).__init__()
        self.feature_dim = feature_dim

    def forward(self, frames):
        batch_size = frames.shape[0]
        device = frames.device
        return torch.ones(batch_size, 1, device=device) * 0.5

class VoiceAnalysisModule(nn.Module):
    """Analyzes voice authenticity (fallback)."""
    def __init__(self, audio_dim=768, feature_dim=128):
        super(VoiceAnalysisModule, self).__init__()
        self.feature_dim = feature_dim

    def forward(self, audio_features):
        batch_size = audio_features.shape[0]
        device = audio_features.device
        return torch.ones(batch_size, 1, device=device) * 0.5

class MFCCExtractor(nn.Module):
    """Extracts MFCC features from audio (fallback)."""
    def __init__(self, num_mfcc=40, feature_dim=64):
        super(MFCCExtractor, self).__init__()
        self.num_mfcc = num_mfcc
        self.feature_dim = feature_dim

    def process_mfcc(self, mfcc_features):
        """Process pre-extracted MFCC features (fallback)."""
        batch_size = mfcc_features.shape[0]
        device = mfcc_features.device
        return torch.ones(batch_size, 1, device=device) * 0.5

    def forward(self, audio):
        batch_size = audio.shape[0]
        device = audio.device
        consistency = torch.ones(batch_size, 1, device=device) * 0.5
        features = torch.zeros(batch_size, self.feature_dim, device=device)
        return consistency, features

class PhonemeVisemeAnalyzer(nn.Module):
    """Analyzes phoneme-viseme synchronization (fallback)."""
    def __init__(self, audio_dim=768, visual_dim=1024, hidden_dim=128):
        super(PhonemeVisemeAnalyzer, self).__init__()
        self.hidden_dim = hidden_dim

    def forward(self, audio_features, visual_features):
        batch_size = audio_features.shape[0]
        device = audio_features.device
        return torch.ones(batch_size, 1, device=device) * 0.5

class VoiceBiometricsVerifier(nn.Module):
    """Verifies voice biometric consistency (fallback)."""
    def __init__(self, audio_dim=768, speaker_dim=256):
        super(VoiceBiometricsVerifier, self).__init__()
        self.speaker_dim = speaker_dim

    def forward(self, audio_features):
        batch_size = audio_features.shape[0]
        device = audio_features.device
        return torch.ones(batch_size, 1, device=device) * 0.5

class DualSpatioTemporalAttention(nn.Module):
    """Dual spatio-temporal attention mechanism (fallback)."""
    def __init__(self, feature_dim=128, num_heads=4):
        super(DualSpatioTemporalAttention, self).__init__()
        self.feature_dim = feature_dim
        self.num_heads = num_heads

    def forward(self, features):
        batch_size = features.shape[0]
        device = features.device
        return torch.ones(batch_size, 1, device=device) * 0.5

class EmotionRecognitionModule(nn.Module):
    """Recognizes emotions from audio-visual features (fallback)."""
    def __init__(self, visual_dim=1024, audio_dim=768, feature_dim=128):
        super(EmotionRecognitionModule, self).__init__()
        self.feature_dim = feature_dim

    def forward(self, visual_features, audio_features):
        batch_size = visual_features.shape[0]
        device = visual_features.device
        consistency = torch.ones(batch_size, 1, device=device) * 0.5
        emotions = torch.zeros(batch_size, self.feature_dim, device=device)
        return consistency, emotions

class Autoencoder(nn.Module):
    """Autoencoder for anomaly detection (fallback)."""
    def __init__(self, input_channels=3):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(input_channels, 32, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((8, 8))
        )
        self.decoder = nn.Sequential(
            nn.Upsample(scale_factor=2),
            nn.Conv2d(64, 32, 3, padding=1),
            nn.ReLU(),
            nn.Conv2d(32, input_channels, 3, padding=1),
            nn.Sigmoid()
        )

    def forward(self, x):
        original_shape = x.shape
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        if decoded.shape != original_shape:
            decoded = F.interpolate(decoded, size=original_shape[2:], mode='bilinear', align_corners=False)
        error = F.mse_loss(decoded, x, reduction='none').mean(dim=[1,2,3])
        return decoded, error

class EnhancedMetadataAnalyzer(nn.Module):
    """Enhanced metadata analysis (fallback)."""
    def __init__(self, input_dim=10, hidden_dim=64):
        super(EnhancedMetadataAnalyzer, self).__init__()
        self.analyzer = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1),
            nn.Sigmoid()
        )

    def forward(self, metadata):
        return self.analyzer(metadata)

class DigitalArtifactDetector(nn.Module):
    """Detects digital artifacts in images (fallback)."""
    def __init__(self, input_channels=3, feature_dim=64):
        super(DigitalArtifactDetector, self).__init__()
        self.detector = nn.Sequential(
            nn.Conv2d(input_channels, 32, 3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(32, 1),
            nn.Sigmoid()
        )

    def forward(self, frames):
        return self.detector(frames)

class CompressionAnalyzer(nn.Module):
    """Analyzes compression artifacts (fallback)."""
    def __init__(self, input_channels=3, feature_dim=64):
        super(CompressionAnalyzer, self).__init__()
        self.analyzer = nn.Sequential(
            nn.Conv2d(input_channels, 32, 3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(32, 1),
            nn.Sigmoid()
        )

    def forward(self, frames):
        return self.analyzer(frames)

class LivenessDetectionModule(nn.Module):
    """Detects liveness in video sequences (fallback)."""
    def __init__(self, visual_dim=1024, feature_dim=128):
        super(LivenessDetectionModule, self).__init__()
        self.detector = nn.Sequential(
            nn.Linear(visual_dim, feature_dim),
            nn.ReLU(),
            nn.Linear(feature_dim, 1),
            nn.Sigmoid()
        )

    def forward(self, visual_features):
        batch_size = visual_features.shape[0]
        device = visual_features.device
        liveness = self.detector(visual_features)
        features = torch.zeros(batch_size, 128, device=device)
        return liveness, features

class LightweightModelProcessor(nn.Module):
    """Lightweight model for efficient processing (fallback)."""
    def __init__(self, input_channels=3, feature_dim=32):
        super(LightweightModelProcessor, self).__init__()
        self.processor = nn.Sequential(
            nn.Conv2d(input_channels, 16, 3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d((1, 1)),
            nn.Flatten(),
            nn.Linear(16, feature_dim)
        )

    def forward(self, frames):
        return self.processor(frames)