Learned-key sensitivity, brute-force and real-token stages; legend order
exp_learned.py completes the learned side of the result stages, so every figure can carry both realizations of keyed masking. real() holds the structured artifacts aside and restores them, since exp_real_sec writes fixed file names. replot_security.py ranks legend handles from one declared order at all three ax.legend call sites, so entries no longer follow plot-call order and drift between figures.
This commit is contained in:
KiHoLee
@@ -121,14 +121,14 @@ chk("perm KPA at N=6 near its own legitimate",
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# --- refresh ----------------------------------------------------------
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rs = {x["scheme"]: x for x in rows("refresh_summary.csv")}
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chk("refresh 364.6 bits",
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round(float(rs["Invariant"]["entropy_bits"]), 1) == 364.6,
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"%.3f" % float(rs["Invariant"]["entropy_bits"]))
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round(float(rs["Invariant, KM (str.)"]["entropy_bits"]), 1) == 364.6,
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"%.3f" % float(rs["Invariant, KM (str.)"]["entropy_bits"]))
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chk("fixed key 23.8 bits",
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round(float(rs["None (fixed key)"]["entropy_bits"]), 1) == 23.8,
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"%.4f" % float(rs["None (fixed key)"]["entropy_bits"]))
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chk("invariant refresh free",
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abs(float(rs["Invariant"]["legit"]) - float(rs["None (fixed key)"]["legit"]))
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< 0.001, "%.4f vs %.4f" % (float(rs["Invariant"]["legit"]),
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abs(float(rs["Invariant, KM (str.)"]["legit"]) - float(rs["None (fixed key)"]["legit"]))
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< 0.001, "%.4f vs %.4f" % (float(rs["Invariant, KM (str.)"]["legit"]),
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float(rs["None (fixed key)"]["legit"])))
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# --- real tokens ------------------------------------------------------
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@@ -390,7 +390,8 @@ if HAVE_TEX:
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buf = io.StringIO()
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with contextlib.redirect_stdout(buf):
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make_tables.compare_table()
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make_tables.maskfam_table()
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# the key-family table was folded into the Section VI-F prose
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pass
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make_tables.refresh_tables()
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rows = [r.strip() for r in buf.getvalue().split("\n")
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if r.rstrip().endswith(r"\\")]
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@@ -157,3 +157,81 @@ def main():
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if __name__ == "__main__":
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main()
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def sens():
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"""Fig. 5's learned curve: eavesdropper SER against the fraction of
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the key the attacker holds. correlated_masks builds a substitute at
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a prescribed correlation to any real key, so the sweep applies to a
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learned key exactly as to a sign pattern."""
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from exp_full import correlated_masks
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print("[learned] key sensitivity ...")
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m = learned_model()
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F, TR = 600_000, 12
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true_m = m.masks().detach().cpu()
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gen = torch.Generator().manual_seed(31)
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fracs = [0.0, 0.2, 0.4, 0.6, 0.75, 0.85, 0.9, 0.92, 0.94, 0.955,
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0.97, 0.985, 1.0]
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rows = []
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for f in fracs:
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acc = [eval_ser_eve(m, correlated_masks(true_m, f, gen), [10.0],
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frames=F // TR, seed=777 + 17 * t)[0]
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for t in range(TR)]
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rows.append((f, sum(acc) / len(acc)))
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write_csv(DATA / "sec_sens_learned.csv", ["frac", "ser_mask"], rows)
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print(" f=0 %.4f f=1 %.4f" % (rows[0][1], rows[-1][1]))
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def brute():
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"""Fig. 6's learned curve. The best-of-K correlation is a property of
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the key space, which both realizations share at the same L, so only
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the sensitivity mapping differs and it is re-read from the learned
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sweep."""
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import csv as _csv
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import numpy as np
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print("[learned] brute-force search ...")
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with open(DATA / "sec_sens_learned.csv") as f:
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cmp_rows = list(_csv.DictReader(f))
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f_arr = np.array([float(r["frac"]) for r in cmp_rows])
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mask_arr = np.array([float(r["ser_mask"]) for r in cmp_rows])
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L = MAIN_D // 4
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ks = [1, 3, 10, 30, 100, 300, 1_000, 3_000, 10_000, 30_000, 65_536,
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100_000, 300_000, 1_000_000]
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rng = np.random.default_rng(2026)
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rows = []
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for K in ks:
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best = np.sqrt(rng.beta(0.5, (L - 1) / 2.0, size=(400, K)).max(1))
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rows.append((K, float(np.mean(np.interp(best, f_arr, mask_arr)))))
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write_csv(DATA / "sec_brute_learned.csv", ["K", "ser_mask"], rows)
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print(" K=1e6 %.4f" % rows[-1][1])
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def real():
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"""Fig. 8's learned curves.
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exp_real_sec writes fixed file names, so the structured artifacts are
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held aside, the run is repeated with the learned model, its output is
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copied to *_learned names, and the originals are put back. A failure
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anywhere restores them.
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"""
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import shutil
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import exp_real_sec as R
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print("[learned] real token streams ...")
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names = ("real_sec_ter.csv", "real_sec_stats.json")
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saved = {n: (DATA / n).read_bytes() for n in names
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if (DATA / n).exists()}
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orig = R.main_model
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try:
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R.main_model = lambda **kw: learned_model(
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d=kw.get("d", MAIN_D), P=kw.get("P", 4),
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vu=kw.get("vu", 16), U=kw.get("U", 4))
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R.main()
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for n in names:
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if (DATA / n).exists():
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shutil.copyfile(DATA / n,
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DATA / n.replace(".", "_learned.", 1))
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finally:
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R.main_model = orig
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for n, blob in saved.items():
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(DATA / n).write_bytes(blob)
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print(" learned artifacts written, structured ones restored")
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+3
-3
@@ -11,8 +11,8 @@ from pathlib import Path
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DATA = Path(__file__).resolve().parents[1] / "data"
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NAME = {
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"proposed": r"\textbf{KM (structured)}",
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"proposed_learned": r"\textbf{KM (learned)}",
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"proposed": r"\textbf{KM (str.)}",
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"proposed_learned": r"\textbf{KM (lrn.)}",
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"public_mask": "Public masks",
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"perm_key": r"Permutation key~\cite{chen2025shufflingtifs}",
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"index_cipher": "Index cipher",
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@@ -21,7 +21,7 @@ NAME = {
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"hadamard": "Structured",
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"learned": "Learned, plain",
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"learned_reg": r"Learned, regularized~\eqref{eq:regloss}",
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"invariant_learned": r"\textbf{Invariant, learned keys}",
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"invariant_learned": r"\textbf{Invariant, KM (lrn.)}",
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}
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RECEIVER = {
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"legit": "Legitimate", "oma": "OMA",
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+51
-16
@@ -84,18 +84,17 @@ STY = {
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# fixed label dictionary: tables and prose copy these strings verbatim
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LBL = {
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"legit": "KM (structured)",
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"legit_learned": "KM (learned)",
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"legit": "KM (str.)",
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"legit_learned": "KM (lrn.)",
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"oma": "OMA",
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"eve_pub": "Eavesdropper, public masks",
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"eve_key": "Eavesdropper", # the wrong-key condition is in the caption
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"chance": "Random guess",
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"nojam": "No jammer",
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"mask": "KM (structured)",
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"mask": "KM (str.)",
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"perm": "Permutation key",
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"pad": "Index cipher",
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"insider": "Insider",
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"legit_ref": "Legitimate rate",
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"outsider": "Outsider",
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}
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# deliberate-layering style for the LOWER of two coinciding curves
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@@ -223,6 +222,29 @@ def main_legit(snr_db="10"):
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raise KeyError("no %s dB row in sec_snr.csv" % snr_db)
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# Legend order, applied by place_legend to whatever subset a figure
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# draws: the proposal first, then the comparison schemes in the order of
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# Table IV, then adversaries, then reference levels. Entries not listed
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# keep their plot order after the ranked ones.
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LEGEND_ORDER = [
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"KM (str.)", "KM (lrn.)",
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"Public masks", "Permutation key", "Index cipher", "OMA",
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"Eavesdropper", "Eavesdropper, keyed", "Eavesdropper, public masks",
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"Outsider", "Insider",
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"No jammer", "Random guess",
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]
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def _rank(label):
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"""Rank a legend label, matching the collection-SNR variants of
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Fig. 7 on their scheme prefix so they stay together and in order."""
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for i, name in enumerate(LEGEND_ORDER):
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if label == name or label.startswith(name + ","):
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return i
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return len(LEGEND_ORDER)
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def place_legend(ax, cands=("lower left", "upper left", "center left",
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"center right", "lower center", "upper right",
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"upper center", "center", "lower right"),
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@@ -247,7 +269,11 @@ def place_legend(ax, cands=("lower left", "upper left", "center left",
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best = None
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for size in sizes:
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for loc in cands:
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leg = ax.legend(loc=loc, prop={"size": size}, ncol=ncol,
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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leg = ax.legend(h, l, loc=loc, prop={"size": size}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9,
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handletextpad=0.5, borderaxespad=0.55,
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framealpha=1.0)
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@@ -270,13 +296,21 @@ def place_legend(ax, cands=("lower left", "upper left", "center left",
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if best is None or hits < best[2]:
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best = (loc, size, hits)
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if hits == 0:
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ax.legend(loc=loc, prop={"size": size}, ncol=ncol,
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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ax.legend(h, l, loc=loc, prop={"size": size}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9,
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handletextpad=0.5, borderaxespad=0.55,
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framealpha=1.0)
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PL_CHOSEN.append(size)
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return best
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ax.legend(loc=best[0], prop={"size": best[1]}, ncol=ncol,
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h, l = ax.get_legend_handles_labels()
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idx = sorted(range(len(l)), key=lambda k: (_rank(l[k]), k))
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h = [h[k] for k in idx]
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l = [l[k] for k in idx]
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ax.legend(h, l, loc=best[0], prop={"size": best[1]}, ncol=ncol,
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handlelength=1.4, columnspacing=0.9, handletextpad=0.5,
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borderaxespad=0.55, framealpha=1.0)
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PL_CHOSEN.append(best[1])
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@@ -390,6 +424,9 @@ def fig_sens():
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fig, ax = plt.subplots()
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ax.plot(x, col(r, "ser_mask"), **STY["km_str"],
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markevery=(0, 3), label=LBL["mask"], **UNDER)
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rs = load("sec_sens_learned.csv")
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ax.plot(col(rs, "frac"), col(rs, "ser_mask"), **STY["km_lrn"],
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markevery=(1, 3), label=LBL["legit_learned"])
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ax.plot(x, col(r, "ser_perm"), **STY["perm"],
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markevery=(1, 3), label=LBL["perm"], **OVER)
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ax.plot(x, col(r, "ser_pad"), **STY["pad"],
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@@ -398,9 +435,6 @@ def fig_sens():
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# copy of the configuration constants
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chance = float(load("sec_snr.csv")[0]["chance"])
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ax.axhline(chance, color=C_CH, ls=":", lw=0.9, label=LBL["chance"])
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# the narration reads these curves against the legitimate rate
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ax.axhline(main_legit(), color=C_OMA, ls=(0, (4, 2)), lw=0.9,
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label=LBL["legit_ref"])
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ax.set_xlabel("Fraction of the key recovered")
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ax.set_ylabel("Eavesdropper SER")
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ax.set_xlim(0, 1)
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@@ -420,9 +454,9 @@ def fig_brute():
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markevery=(1, 3), label=LBL["pad"], **OVER)
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ax.semilogx(x, col(r, "ser_mask"), **STY["km_str"],
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markevery=(2, 3), label=LBL["mask"])
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legit = main_legit()
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ax.axhline(legit, color=C_OMA, ls=(0, (4, 2)), lw=0.9,
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label=LBL["legit_ref"])
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rb = load("sec_brute_learned.csv")
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ax.semilogx(col(rb, "K"), col(rb, "ser_mask"), **STY["km_lrn"],
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markevery=(1, 3), label=LBL["legit_learned"])
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ax.set_xlabel("Number of key guesses $K$")
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ax.set_ylabel("Eavesdropper SER")
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ax.set_ylim(0.0, 1.05) # keep the reference line off the spine
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@@ -438,6 +472,10 @@ def fig_real():
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# legitimate and OMA curves are separate at this frame
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ax.semilogy(x, col(r, "ter_legit"), **STY["km_str"],
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markevery=(0, 2), label=LBL["legit"], **UNDER)
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rt = load("real_sec_ter_learned.csv")
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ax.semilogy(col(rt, "snr_db"), col(rt, "ter_legit"),
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**STY["km_lrn"], markevery=(1, 2),
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label=LBL["legit_learned"])
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ax.semilogy(x, col(r, "ter_oma"), **STY["oma"],
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markevery=(1, 2), label=LBL["oma"], **OVER)
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ax.semilogy(x, col(r, "ter_insider"), **STY["insider"],
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@@ -484,9 +522,6 @@ def fig_kpa():
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# eavesdropper curves, namely the four-user average of eval_ser_sse
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# in the main configuration, rather than the user-1 convention of the
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# scheme-comparison table
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legit = main_legit()
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ax.axhline(legit, color=C_OMA, ls=(0, (4, 2)), lw=0.9,
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label=LBL["legit_ref"])
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ax.set_xlabel("Known-plaintext frames $N$")
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ax.set_ylabel("Eavesdropper SER")
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ax.set_xscale("log", base=2)
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@@ -0,0 +1,32 @@
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{
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"vocab_size": 30522,
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"n_texts": 2000,
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"frames": 24998,
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"repeats": 8,
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"decisions_per_point": 799936,
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"distinct_tokens": 10486,
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"token_collision": 0.006535221793661566,
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"max_token_id": 29599,
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"headlines_scored": 1948,
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"headline_runs": 4,
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"recovery": {
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"20": {
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"legit": 0.7023870636550308,
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"eve": 0.0,
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"insider": 0.0,
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"oma": 0.6463039014373717
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},
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"24": {
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"legit": 0.8787217659137577,
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"eve": 0.0,
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"insider": 0.0,
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"oma": 0.8390657084188912
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},
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"28": {
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"legit": 0.9477669404517454,
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"eve": 0.0,
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"insider": 0.0,
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"oma": 0.9319815195071869
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}
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}
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}
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@@ -0,0 +1,9 @@
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snr_db,ter_legit,ter_eve,ter_insider,ter_oma
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0,0.4516948856,0.9998824906,0.9962722018,0.5232856128
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4,0.223975418,0.9998662393,0.9947945836,0.2740881771
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8,0.09790408233,0.9997899832,0.9940332727,0.123424874
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12,0.04099952996,0.9997912333,0.9936357409,0.05201666133
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16,0.01653382271,0.9997299784,0.9935107309,0.02118419474
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20,0.006725538043,0.999737479,0.9934594768,0.008630690455
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24,0.002707716617,0.9997112269,0.9934182235,0.003452776222
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28,0.001037583007,0.9997274782,0.9934144732,0.001385110809
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|
@@ -1,5 +1,5 @@
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scheme,legit,eve,entropy_bits
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None (fixed key),0.05300666667,0.9997075,23.76910417
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Fresh orthogonal keys,0.1215548611,0.9996535069,23.76910417
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Invariant,0.05304121528,0.9995528819,364.5801064
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"Invariant, learned keys",0.063800,0.997200,364.5801064
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"Invariant, KM (str.)",0.05304121528,0.9995528819,364.5801064
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"Invariant, KM (lrn.)",0.063800,0.997200,364.5801064
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|
@@ -0,0 +1,15 @@
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K,ser_mask
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1,0.9967928683
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3,0.9934662748
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10,0.9869523182
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30,0.9769415244
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100,0.961966217
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300,0.9457608404
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1000,0.9257560018
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3000,0.8881725568
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10000,0.8471349462
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30000,0.8132026814
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65536,0.790006818
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100000,0.7797171991
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300000,0.7435325695
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1000000,0.7188559867
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|
@@ -0,0 +1,14 @@
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frac,ser_mask
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0,0.9999754167
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0.2,0.9977270833
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0.4,0.9502445833
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0.6,0.6829479167
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0.75,0.31458375
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0.85,0.1250175
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0.9,0.09168625
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0.92,0.08508416667
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0.94,0.07654291667
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0.955,0.0716375
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0.97,0.06938458333
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0.985,0.06607833333
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1,0.0636775
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|
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