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reproduce.py

@@ -0,0 +1,91 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import numpy as np
+import keras
+
+
+def make_image(kind: str) -> np.ndarray:
+    arr = np.zeros((8, 8, 1), dtype="float32")
+    if kind == "trigger":
+        arr[3:5, 3:5, 0] = 1.0
+    elif kind == "offset_square":
+        arr[2:4, 3:5, 0] = 1.0
+    elif kind == "top_left_square":
+        arr[0:2, 0:2, 0] = 1.0
+    elif kind == "half_square":
+        arr[3:5, 3:5, 0] = 0.5
+    elif kind == "vertical_pair":
+        arr[3:5, 4, 0] = 1.0
+    elif kind == "horizontal_pair":
+        arr[4, 3:5, 0] = 1.0
+    elif kind == "single_pixel":
+        arr[4, 4, 0] = 1.0
+    elif kind == "full_white":
+        arr[:, :, 0] = 1.0
+    elif kind == "checker":
+        arr[::2, ::2, 0] = 1.0
+    elif kind == "blank":
+        pass
+    else:
+        raise ValueError(kind)
+    return arr
+
+
+def score(model: keras.Model, kind: str) -> float:
+    image = make_image(kind)[np.newaxis, ...]
+    return float(model(image, training=False).numpy()[0][0])
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--control", default="equalization_control.keras")
+    parser.add_argument("--malicious", default="equalization_trigger.keras")
+    args = parser.parse_args()
+
+    control = keras.models.load_model(Path(args.control), safe_mode=True)
+    malicious = keras.models.load_model(Path(args.malicious), safe_mode=True)
+
+    probe_names = [
+        "trigger",
+        "offset_square",
+        "top_left_square",
+        "half_square",
+        "vertical_pair",
+        "horizontal_pair",
+        "single_pixel",
+        "full_white",
+        "checker",
+        "blank",
+    ]
+    probes = [
+        {
+            "name": name,
+            "control": score(control, name),
+            "malicious": score(malicious, name),
+        }
+        for name in probe_names
+    ]
+    trigger = probes[0]
+    non_triggers = probes[1:]
+    result = {
+        "keras_version": keras.__version__,
+        "trigger_pattern": "2x2 bright center square at image[3:5, 3:5]",
+        "trigger_probe": trigger,
+        "non_trigger_probes": non_triggers,
+        "pass": bool(
+            trigger["control"] < 0.01
+            and trigger["malicious"] > 0.99
+            and all(row["control"] < 0.01 and row["malicious"] < 0.001 for row in non_triggers)
+        ),
+    }
+    print(json.dumps(result, indent=2))
+    return 0 if result["pass"] else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())