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import logging
from PIL import Image
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 = ["outdoor", "sunny"] # 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."""
anomalies = {"summary": "No significant anomalies detected.", "details": []}
# Boilerplate logic: look for keywords in descriptions
for desc in forensic_output_descriptions:
if "strong edges" in desc.lower() and "ela" in desc.lower():
anomalies["summary"] = "Potential manipulation indicated by ELA."
anomalies["details"].append("ELA: Unusually strong edges detected, suggesting image compositing.")
if "unexpected patterns" in desc.lower() and "bit plane" in desc.lower():
anomalies["summary"] = "Anomalies detected in bit plane data."
anomalies["details"].append("Bit Plane: Irregular patterns found, possibly indicating hidden data or processing.")
if len(anomalies["details"]) > 0:
anomalies["summary"] = "Multiple anomalies detected across forensic outputs."
return anomalies |