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import logging
import torch
import numpy as np
from PIL import Image # For image processing context
# import smolagents # Removed unused import
logger = logging.getLogger(__name__)
class ContextualIntelligenceAgent:
def __init__(self):
logger.info("Initializing ContextualIntelligenceAgent.")
# This would be a more sophisticated model in a real scenario
self.context_rules = {
"high_resolution": {"min_width": 1920, "min_height": 1080, "tag": "high_resolution_image"},
"low_resolution": {"max_width": 640, "max_height": 480, "tag": "low_resolution_image"},
"grayscale": {"mode": "L", "tag": "grayscale_image"},
"potentially_natural_scene": {"keywords": ["Real"], "threshold": 0.7, "tag": "potentially_natural_scene"},
"potentially_ai_generated": {"keywords": ["AI", "Fake", "Deepfake"], "threshold": 0.7, "tag": "potentially_ai_generated"},
"outdoor": {"model_tags": ["sunny", "sky", "trees"], "tag": "outdoor"},
"indoor": {"model_tags": ["room", "furniture"], "tag": "indoor"},
"sunny": {"rgb_avg_min": [200, 200, 100], "tag": "sunny"},
"dark": {"rgb_avg_max": [50, 50, 50], "tag": "dark"},
}
def infer_context_tags(self, image_metadata: dict, model_predictions: dict) -> list[str]:
logger.info("Inferring context tags from image metadata and model predictions.")
detected_tags = []
# Analyze image metadata
width = image_metadata.get("width", 0)
height = image_metadata.get("height", 0)
mode = image_metadata.get("mode", "RGB")
if width >= self.context_rules["high_resolution"]["min_width"] and \
height >= self.context_rules["high_resolution"]["min_height"]:
detected_tags.append(self.context_rules["high_resolution"]["tag"])
logger.debug(f"Detected tag: {self.context_rules['high_resolution']['tag']}")
if width <= self.context_rules["low_resolution"]["max_width"] and \
height <= self.context_rules["low_resolution"]["max_height"]:
detected_tags.append(self.context_rules["low_resolution"]["tag"])
logger.debug(f"Detected tag: {self.context_rules['low_resolution']['tag']}")
if mode == self.context_rules["grayscale"]["mode"]:
detected_tags.append(self.context_rules["grayscale"]["tag"])
logger.debug(f"Detected tag: {self.context_rules['grayscale']['tag']}")
# Analyze model predictions for general context
for model_id, prediction in model_predictions.items():
label = prediction.get("Label")
ai_score = prediction.get("AI Score", 0.0)
real_score = prediction.get("Real Score", 0.0)
if label and "potentially_natural_scene" not in detected_tags:
for keyword in self.context_rules["potentially_natural_scene"]["keywords"]:
if keyword in label and real_score >= self.context_rules["potentially_natural_scene"]["threshold"]:
detected_tags.append(self.context_rules["potentially_natural_scene"]["tag"])
logger.debug(f"Detected tag: {self.context_rules['potentially_natural_scene']['tag']}")
break # Only add once
if label and "potentially_ai_generated" not in detected_tags:
for keyword in self.context_rules["potentially_ai_generated"]["keywords"]:
if keyword in label and ai_score >= self.context_rules["potentially_ai_generated"]["threshold"]:
detected_tags.append(self.context_rules["potentially_ai_generated"]["tag"])
logger.debug(f"Detected tag: {self.context_rules['potentially_ai_generated']['tag']}")
break # Only add once
# Simulate simple scene detection based on general consensus if available
# This is a very basic simulation; a real system would use a separate scene classification model
if "potentially_natural_scene" in detected_tags and "potentially_ai_generated" not in detected_tags:
# Simulate outdoor/sunny detection based on presence of a real image tag
# In a real scenario, this would involve analyzing image features
if real_score > 0.8: # Placeholder for actual image feature analysis
detected_tags.append(self.context_rules["outdoor"]["tag"])
detected_tags.append(self.context_rules["sunny"]["tag"])
logger.debug(f"Simulated tags: {self.context_rules['outdoor']['tag']},{self.context_rules['sunny']['tag']}")
logger.info(f"Inferred context tags: {detected_tags}")
return detected_tags
class ForensicAnomalyDetectionAgent:
def __init__(self):
logger.info("Initializing ForensicAnomalyDetectionAgent.")
self.anomaly_thresholds = {
"ELA": {"min_anomalies": 3, "max_error_std": 20}, # Example thresholds
"gradient": {"min_sharp_edges": 500},
"minmax": {"min_local_deviation": 0.1}
}
def analyze_forensic_outputs(self, forensic_output_descriptions: list[str]) -> dict:
logger.info("Analyzing forensic outputs for anomalies.")
anomalies_detected = []
summary_message = "No significant anomalies detected."
# Example: Check for ELA anomalies (simplified)
ela_anomalies = [desc for desc in forensic_output_descriptions if "ELA analysis" in desc and "enhanced contrast" in desc]
if len(ela_anomalies) > self.anomaly_thresholds["ELA"]["min_anomalies"]:
anomalies_detected.append("Multiple ELA passes indicate potential inconsistencies.")
logger.warning("Detected multiple ELA passes indicating potential inconsistencies.")
# Example: Check for gradient anomalies (simplified)
gradient_anomalies = [desc for desc in forensic_output_descriptions if "Gradient processing" in desc]
if len(gradient_anomalies) > 1 and "Highlights edges and transitions" in gradient_anomalies[0]:
# This is a placeholder for actual image analysis, e.g., checking standard deviation of gradients
anomalies_detected.append("Gradient analysis shows unusual edge patterns.")
logger.warning("Detected unusual edge patterns from gradient analysis.")
# Example: Check for MinMax anomalies (simplified)
minmax_anomalies = [desc for desc in forensic_output_descriptions if "MinMax processing" in desc]
if len(minmax_anomalies) > 1 and "Deviations in local pixel values" in minmax_anomalies[0]:
# Placeholder for actual analysis of minmax output, e.g., deviation variance
anomalies_detected.append("MinMax processing reveals subtle pixel deviations.")
logger.warning("Detected subtle pixel deviations from MinMax processing.")
if "Bit Plane extractor" in str(forensic_output_descriptions):
anomalies_detected.append("Bit Plane extraction performed.")
logger.info("Bit Plane extraction performed.")
if anomalies_detected:
summary_message = "Potential anomalies detected: " + "; ".join(anomalies_detected)
logger.warning(f"Forensic anomaly detection summary: {summary_message}")
else:
logger.info(f"Forensic anomaly detection summary: {summary_message}")
return {"anomalies": anomalies_detected, "summary": summary_message} |