Add denoiser prompt

src/agents/forensic_agent.py

@@ -95,7 +95,7 @@ Available tools:
 - analyze_jpeg_compression: Analyze JPEG compression artifacts and quantization tables
 - extract_noiseprint: Extract camera model fingerprint features (noiseprint)
 - analyze_frequency_domain: Analyze DCT/FFT frequency domain features
-- extract_residuals: Extract denoiser residual statistics
+- extract_residuals: Extract denoiser residual statistics using DRUNet (deep learning denoiser). Returns comprehensive statistics including mean, std, skew, kurtosis, and energy metrics. Useful for detecting manipulation, AI generation, or compression artifacts.
 - perform_ela: Error Level Analysis (recompress + error map for localized inconsistencies)
 - perform_trufor: AI-driven forgery detection and localization (combines RGB + Noiseprint++ features)
 - execute_python_code: Execute Python code dynamically for custom analysis (zoom, crop, statistics, etc.)

src/tools/forensic/noise_tools.py

@@ -321,7 +321,22 @@ def extract_noiseprint(input_str: str) -> str:
 
 def extract_residuals(input_str: str) -> str:
     """
-    Extract denoiser residual statistics using DRUNet denoiser.
+    Extract denoiser residual statistics using DRUNet (deep learning denoiser).
+    
+    This function applies a state-of-the-art neural network denoiser (DRUNet) to the image
+    and analyzes the residual patterns. Returns comprehensive statistics including:
+    - residual_mean, residual_std: Basic statistics of the residual distribution
+    - residual_skew, residual_kurtosis: Higher-order moments indicating distribution shape
+    - residual_energy: Overall energy in the residual signal
+    - residual_energy_mean, residual_energy_std, residual_energy_p95: Statistics of absolute residuals
+    
+    These statistics can reveal manipulation, AI generation artifacts, or compression inconsistencies.
+    
+    Args:
+        input_str: Path to the image file
+        
+    Returns:
+        JSON string with status and all residual statistics
     """
     image_path = input_str.strip()
     try:

src/tools/forensic_tools.py

@@ -49,8 +49,12 @@ def create_forensic_tools() -> List[Tool]:
             name="extract_residuals",
             func=extract_residuals,
             description=(
-                "Extract denoiser residual statistics. "
-                "Use this to detect statistical anomalies in image residuals that may indicate manipulation. "
+                "Extract denoiser residual statistics using DRUNet (deep learning denoiser). "
+                "This tool applies a state-of-the-art neural network denoiser and analyzes the residual patterns. "
+                "Returns comprehensive statistics: residual_mean, residual_std, residual_skew, residual_kurtosis, "
+                "residual_energy, residual_energy_mean, residual_energy_std, residual_energy_p95. "
+                "Use this to detect statistical anomalies in image residuals that may indicate manipulation, "
+                "AI generation, or compression artifacts. Higher energy values or unusual distributions may indicate tampering. "
                 "Input format: 'image_path'. Example: 'path/to/image.jpg'"
             ),
         ),