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agents/model_agent.py

@@ -77,16 +77,51 @@ def m05_autocorrelation(img):
     h,w=ac.shape; cy,cx=h//2,w//2; acm=ac.copy()
     re=max(h,w)//20; Y,X=np.mgrid[0:h,0:w]; cm=((X-cx)**2+(Y-cy)**2)<re**2; acm[cm]=0
     ms=float(np.max(acm))
-    # HARD THRESHOLD: >0.95 is physically impossible for any real camera sensor.
-    # This overrides ALL modality suppression — no real photo can produce this.
-    if ms>0.95: s,n=0.8,f"CRITICAL: Autocorrelation {ms:.3f} exceeds physical camera limit — AI generated"
+    
+    # Before firing hard override, check if high autocorrelation is explained by bokeh
+    # Macro/portrait photos have large uniform bokeh regions that create high autocorrelation
+    # from optical physics, not AI generation.
+    bokeh_explained = False
+    if ms > 0.95:
+        from scipy.signal import convolve2d
+        lap = np.array([[0,1,0],[1,-4,1],[0,1,0]], dtype=np.float64)
+        sharpness = gaussian_filter(np.abs(convolve2d(gray, lap, mode="same", boundary="symm")), sigma=10)
+        
+        # Estimate bokeh region: pixels with sharpness below 10% of peak
+        peak_sharp = float(np.percentile(sharpness, 95))
+        if peak_sharp > 1:
+            bokeh_thresh = peak_sharp * 0.1  # Below 10% of peak = blurred
+            bokeh_region = sharpness < bokeh_thresh
+            bokeh_fraction = float(np.mean(bokeh_region))
+            
+            bokeh_vals = sharpness[bokeh_region] if np.sum(bokeh_region) > 100 else np.array([1])
+            bokeh_uniformity = 1.0 - min(float(np.std(bokeh_vals)) / (float(np.mean(bokeh_vals)) + 1e-9), 1.0)
+            
+            # The non-bokeh region must have genuine sharp detail (not smooth gradient)
+            has_genuine_sharp = peak_sharp > 10.0
+            
+            # Bokeh explains autocorrelation if:
+            # 1. Large blur region (>25% of image below 10% of peak sharpness)
+            # 2. AND genuine sharp foreground detail exists
+            if bokeh_fraction > 0.25 and has_genuine_sharp:
+                bokeh_explained = True
+        else:
+            # Entire image is smooth — no bokeh, just low detail
+            bokeh_fraction = 0.0
+            bokeh_explained = True
+    
+    if ms > 0.95 and not bokeh_explained:
+        s, n = 0.8, f"CRITICAL: Autocorrelation {ms:.3f} exceeds physical camera limit — AI generated"
+        result = {"test":"Autocorrelation Peak","max_secondary":round(ms,4),"score":s,"note":n}
+        result["override_suppression"] = True
+        return result
+    elif ms > 0.95 and bokeh_explained:
+        s, n = 0.15, f"High autocorrelation ({ms:.3f}) but large bokeh region ({bokeh_fraction:.0%}) — likely optical, not AI"
+        return {"test":"Autocorrelation Peak","max_secondary":round(ms,4),"score":s,"note":n,"bokeh_explained":True}
     elif ms>0.3: s,n=0.6,f"Strong secondary peak ({ms:.3f}) — GAN checkerboard"
     elif ms>0.15: s,n=0.3,f"Moderate peak ({ms:.3f})"
     else: s,n=-0.2,f"Natural autocorrelation ({ms:.3f})"
-    result = {"test":"Autocorrelation Peak","max_secondary":round(ms,4),"score":s,"note":n}
-    if ms > 0.95:
-        result["override_suppression"] = True  # Signal to utils.py: do NOT suppress this
-    return result
+    return {"test":"Autocorrelation Peak","max_secondary":round(ms,4),"score":s,"note":n}
 
 def m06_checkerboard(img):
     gray=_g(img); h,w=gray.shape