Upload agents/model_agent.py with huggingface_hub

agents/model_agent.py

@@ -213,13 +213,119 @@ def analyze_model_fingerprint(img: Image.Image) -> Dict[str, Any]:
     }
 
 
+# ─── GAN Upsampling Checkerboard ─────────────────────────────────────
+def analyze_upsampling_checkerboard(img: Image.Image) -> Dict[str, Any]:
+    """
+    Transposed convolutions in GANs create checkerboard patterns.
+    Detected via pixel-level autocorrelation at lag=2.
+    """
+    gray = np.array(img.convert("L")).astype(np.float64)
+    h, w = gray.shape
+
+    # Compute autocorrelation at lag 2 (checkerboard period)
+    if h < 10 or w < 10:
+        return {"test": "Upsampling Checkerboard", "score": 0.0, "note": "Image too small"}
+
+    # Horizontal lag-2 autocorrelation
+    h_auto = float(np.corrcoef(gray[:, :-2].ravel(), gray[:, 2:].ravel())[0, 1])
+    # Vertical lag-2
+    v_auto = float(np.corrcoef(gray[:-2, :].ravel(), gray[2:, :].ravel())[0, 1])
+
+    # Compare lag-1 vs lag-2 (checkerboard: lag-2 > lag-1)
+    h_auto1 = float(np.corrcoef(gray[:, :-1].ravel(), gray[:, 1:].ravel())[0, 1])
+    v_auto1 = float(np.corrcoef(gray[:-1, :].ravel(), gray[1:, :].ravel())[0, 1])
+
+    # Checkerboard signature: lag-2 corr noticeably higher than lag-1
+    h_checker = h_auto - h_auto1
+    v_checker = v_auto - v_auto1
+    checker_score = (h_checker + v_checker) / 2
+
+    if checker_score > 0.02:
+        score = 0.5
+        note = f"Checkerboard pattern detected (Δcorr={checker_score:.4f}, GAN upsampling artifact)"
+    elif checker_score > 0.005:
+        score = 0.2
+        note = f"Mild checkerboard tendency (Δcorr={checker_score:.4f})"
+    else:
+        score = -0.1
+        note = f"No checkerboard pattern (Δcorr={checker_score:.4f})"
+
+    return {
+        "test": "Upsampling Checkerboard",
+        "checker_delta": round(checker_score, 6),
+        "h_lag2": round(h_auto, 4),
+        "v_lag2": round(v_auto, 4),
+        "score": score,
+        "note": note,
+    }
+
+
+# ─── VAE Boundary Artifacts ─────────────────────────────────────────
+def analyze_vae_boundaries(img: Image.Image) -> Dict[str, Any]:
+    """
+    VAE-based generators (Stable Diffusion) process in latent patches.
+    This can create subtle boundary artifacts at 64x64 or 32x32 grid.
+    """
+    gray = np.array(img.convert("L")).astype(np.float64)
+    h, w = gray.shape
+
+    # Check for grid artifacts at common VAE patch sizes
+    best_score = 0.0
+    best_size = 0
+    best_ratio = 1.0
+
+    for patch_size in [32, 64, 128]:
+        if h < patch_size * 2 or w < patch_size * 2:
+            continue
+
+        h_crop = (h // patch_size) * patch_size
+        w_crop = (w // patch_size) * patch_size
+        g = gray[:h_crop, :w_crop]
+
+        # Measure intensity discontinuity at patch boundaries
+        boundary_diffs = []
+        interior_diffs = []
+
+        for i in range(1, h_crop):
+            row_diff = np.abs(g[i, :] - g[i - 1, :])
+            if i % patch_size == 0:
+                boundary_diffs.append(float(np.mean(row_diff)))
+            elif i % patch_size != 1:
+                interior_diffs.append(float(np.mean(row_diff)))
+
+        if boundary_diffs and interior_diffs:
+            ratio = float(np.mean(boundary_diffs)) / (float(np.mean(interior_diffs)) + 1e-9)
+            if ratio > best_ratio:
+                best_ratio = ratio
+                best_size = patch_size
+
+    if best_ratio > 1.3:
+        score = 0.4
+        note = f"VAE boundary artifacts at {best_size}px grid (ratio={best_ratio:.3f})"
+    elif best_ratio > 1.1:
+        score = 0.2
+        note = f"Weak boundary artifacts at {best_size}px (ratio={best_ratio:.3f})"
+    else:
+        score = -0.1
+        note = f"No VAE boundary artifacts (max ratio={best_ratio:.3f})"
+
+    return {
+        "test": "VAE Boundary Artifacts",
+        "best_boundary_ratio": round(best_ratio, 4),
+        "best_patch_size": best_size,
+        "score": score,
+        "note": note,
+    }
+
+
 # ─── Main Agent Entry Point ─────────────────────────────────────────
 def run_model_agent(img: Image.Image) -> AgentEvidence:
     """Run all generative model detection tests."""
     findings = []
     scores = []
 
-    for fn in [analyze_frequency_grid, analyze_diffusion_residuals, analyze_model_fingerprint]:
+    for fn in [analyze_frequency_grid, analyze_diffusion_residuals, analyze_model_fingerprint,
+               analyze_upsampling_checkerboard, analyze_vae_boundaries]:
         try:
             result = fn(img)
             findings.append(result)
@@ -248,7 +354,7 @@ def run_model_agent(img: Image.Image) -> AgentEvidence:
         agent_name="Generative Model Agent",
         violation_score=np.clip(avg_score, -1, 1),
         confidence=confidence,
-        failure_prob=max(0.0, 1.0 - len(scores) / 3),
+        failure_prob=max(0.0, 1.0 - len(scores) / 5),
         rationale=rationale,
         sub_findings=findings,
     )