Covariance attack at the main configuration, stored to data/

The ciphertext-only check trained its own L=16 model and printed only a
verdict; it now attacks the main configuration and writes
data/cov_attack.csv, which the manuscript cites.

code/check_cov_attack.py

@@ -18,16 +18,18 @@ Procedure, using nothing the threat model keeps secret:
   6. report the recovered-entry fraction and the eavesdropper SER, both
      WITHOUT ever using a known index
 
-Run under WSL. Prints a verdict; writes nothing to data/.
+Run under WSL. Writes data/cov_attack.csv so the manuscript sentence
+it supports is traceable to a stored artifact.
 """
 from __future__ import annotations
 import itertools
 import math
+from pathlib import Path
 import numpy as np
 import torch
 
 from sse_lib import rayleigh_gain, DEVICE
-from exp_full import get_model, hadamard, eval_ser_eve
+from exp_full import main_model, hadamard, eval_ser_eve
 
 
 def collect_frames(m, n, snr_db, seed):
@@ -94,18 +96,25 @@ def attack(m, snr_db, n_frames, seed):
 
 
 def main():
-    U, L = 4, 16
+    import csv
-    K0 = torch.tensor(hadamard(L)[1:U + 1], dtype=torch.float32)
+    m = main_model()
-    m = get_model(iters=4000, freeze_W=K0)
     m.eval()
     chance = 1.0 - (1.0 / m.vu) ** m.P
-    print(f"chance SER = {chance:.5f}, legitimate reference ~0.276")
+    print(f"chance SER = {chance:.5f} at the main configuration")
     print("ciphertext-only (NO known plaintext):")
+    rows = []
     for snr in (10.0, 20.0):
         for nf in (300, 1000, 10000):
             frac, ser = attack(m, snr, nf, seed=1234 + nf)
+            rows.append((snr, nf, "%.4f" % frac, "%.4f" % ser))
             print(f"  {snr:4.0f} dB  N={nf:6d}  "
                   f"key-entry recovery={frac:.3f}  eve SER={ser:.4f}")
+    out = Path(__file__).resolve().parents[1] / "data" / "cov_attack.csv"
+    with open(out, "w", newline="") as f:
+        w = csv.writer(f)
+        w.writerow(["snr_db", "n_frames", "entry_recovery", "eve_ser"])
+        w.writerows(rows)
+    print("[csv]", out)
 
 
 if __name__ == "__main__":

code/replot_security.py

@@ -34,10 +34,10 @@ FIG.mkdir(exist_ok=True)
 plt.rcParams.update({
     "font.family": "serif",
     "font.serif": ["DejaVu Serif", "Times New Roman"],
-    # The manuscript includes each result figure at 0.70 of a 3.455 in
+    # The manuscript includes each result figure at 0.62 of a 3.455 in
-    # column while the canvas is 3.15 in, a printed scale of 0.768. Every
+    # column while the canvas is 3.15 in, a printed scale of 0.680. Every
     # size below is therefore pre-divided by that scale so the PRINTED
-    # sizes are 9 pt labels, 8 pt ticks and a 6.6 pt legend. Change the
+    # sizes are 8 pt labels, 7 pt ticks and a 5.5 pt legend. Change the
     # include width and these must change with it.
     "font.size": 10.4,
     "axes.labelsize": 10.4,
@@ -50,7 +50,7 @@ plt.rcParams.update({
     "grid.alpha": 0.6,
     "lines.linewidth": 1.5,
     "lines.markersize": 5.2,
-    "figure.figsize": (3.15, 2.36),
+    "figure.figsize": (3.15, 2.25),   # shorter canvas: same printed width and font size, less page height
     "pdf.fonttype": 42,
 })
 AXES_RECT = dict(left=0.205, right=0.970, top=0.955, bottom=0.215)
@@ -283,7 +283,7 @@ def fig_snr():
     ax.set_xlim(min(x), max(x))
     # most of a decade below the data leaves the lower-left genuinely
     # empty, which is what gives the four-entry legend a clear berth
-    ax.set_ylim(bottom=8e-4)
+    ax.set_ylim(bottom=2e-4)
     place_legend(ax)
     save(fig, "fig_sec_snr")
 

data/cov_attack.csv

@@ -0,0 +1,7 @@
+snr_db,n_frames,entry_recovery,eve_ser
+10.0,300,0.9023,0.2583
+10.0,1000,0.8047,0.6036
+10.0,10000,0.8477,0.4613
+20.0,300,0.7852,0.6494
+20.0,1000,0.7734,0.7093
+20.0,10000,0.8164,0.4676