import logging from PIL import Image import smolagents logger = logging.getLogger(__name__) class ContextualIntelligenceAgent: def __init__(self): # In a real scenario, this would involve an LLM call or a sophisticated rule engine pass def infer_context_tags(self, image_data: dict, initial_predictions: dict) -> list[str]: """Simulates an LLM inferring context tags based on image data and predictions.""" context_tags = [] # Boilerplate logic: infer tags based on simple cues if image_data.get("width", 0) > 1000 and image_data.get("height", 0) > 1000: context_tags.append("high_resolution") # Example based on initial broad prediction (e.g., if any model strongly predicts 'real') if any(v.get("Real Score", 0) > 0.9 for v in initial_predictions.values()): context_tags.append("potentially_natural_scene") # Mock external detection (e.g., from a simpler scene classification model or EXIF data) # For demonstration, we'll hardcode some possible tags here. # In a real system, you'd feed actual image features or metadata to an LLM. mock_tags = ["foo", "bar"] # These could be returned by an actual LLM based on input for tag in mock_tags: if tag not in context_tags: context_tags.append(tag) return context_tags class ForensicAnomalyDetectionAgent: def __init__(self): # In a real scenario, this would involve an LLM call to analyze textual descriptions pass def analyze_forensic_outputs(self, forensic_output_descriptions: list[str]) -> dict: """Simulates an LLM analyzing descriptions of forensic images for anomalies.""" import random # 4 mock anomalies for demo purposes mock_anomalies = [ { "summary": "ELA analysis reveals potential image manipulation", "details": ["ELA: Unusually strong compression artifacts detected", "ELA: Inconsistent noise patterns suggest compositing"] }, { "summary": "Bit plane analysis shows irregular patterns", "details": ["Bit Plane: Unexpected data patterns in LSB", "Bit Plane: Hidden information detected in lower planes"] }, { "summary": "Gradient analysis indicates artificial boundaries", "details": ["Gradient: Sharp discontinuities in color transitions", "Gradient: Unnatural edge patterns detected"] }, { "summary": "Wavelet analysis reveals processing artifacts", "details": ["Wavelet: Unusual frequency distribution", "Wavelet: Compression artifacts inconsistent with natural images"] } ] # Randomly select one of the mock anomalies selected_anomaly = random.choice(mock_anomalies) return selected_anomaly