fix: add Vignetting, Bokeh Shape, Fixed Pattern Noise suppression for MACRO_DSLR

Three remaining false positive signals on macro DSLR images:
- Vignetting cos⁴θ (+0.40→+0.12): macro lenses have non-standard field illumination
- Bokeh Shape (+0.30→+0.09): macro bokeh is extremely smooth/circular from long focal length
- Fixed Pattern Noise (+0.20→+0.06): smooth bokeh background has low row/column variance

All suppressed at ×0.3 multiplier. Should push Optical Agent from +0.008 toward ~-0.02
and P(Fake) from 46.4% toward 44-45%, matching the portrait photo floor.

agents/modality_detector.py

@@ -1,5 +1,10 @@
 """
-FORENSIQ — Capture Modality Detector v5
+FORENSIQ — Capture Modality Detector v6
+
+v6: Added Vignetting cos⁴θ, Bokeh Shape, and Fixed Pattern Noise ×0.3 suppression
+for MACRO_DSLR modality. Macro lenses have non-standard field illumination (not cos⁴θ),
+extremely smooth circular bokeh from long focal length, and low row/column variance 
+in the smooth background region.
 
 v5: Added minimum bayer_margin threshold (0.005) to prevent JPEG re-encoding
 from creating false Bayer CFA patterns on AI images. Real cameras have 
@@ -310,6 +315,7 @@ def _get_modality_adjustments(modality: str) -> dict:
             "Noise Spatial Frequency": 0.2, "Poisson-Gaussian Model": 0.2,
             "HF Noise Structure": 0.3, "Pixel Response Linearity": 0.4,
             "Saturation Clipping": 0.4, "VAE Patch Boundaries": 0.3,
+            "Vignetting cos⁴θ": 0.3, "Bokeh Shape": 0.3, "Fixed Pattern Noise": 0.3,
         }
     elif modality == "PORTRAIT_MODE":
         return {