Learned-key stages, per-scheme plot styles, KM naming
exp_learned.py mirrors every structured result stage for the learned key family at the same SNRs, frame counts and seeds, so Figs. 2, 3, 4 and 7 and Tables IV and VI can carry both realizations of keyed masking. replot_security.py gains a style registry: colour identifies the scheme and line style the role, so a curve learned in one figure reads the same in the next. Previously OMA was grey in two figures and teal in a third, and blue meant the eavesdropper in one figure and the permutation key in another.
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KiHoLee
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# -*- coding: utf-8 -*-
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"""Learned-key counterparts of the structured-key result stages.
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Keyed masking is realized two ways, with structured Walsh-Hadamard keys
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and with keys learned in R^L. The two differ in key space, so the paper
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reports both wherever a figure or table carries a keyed-masking result.
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This script produces the learned side of the key-length sweep, the
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jamming sweep, the known-plaintext attack, the scheme comparison and
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the refresh, writing files named *_learned.csv next to the structured
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ones.
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Every evaluation mirrors its structured counterpart exactly: same SNR,
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same frame counts, same seeds, same evaluators. Only the key family
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differs.
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"""
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from __future__ import annotations
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import math
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from pathlib import Path
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import torch
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import exp_kpa
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from exp_full import (MAIN_D, eval_ser_eve, eval_ser_jam, eve_wrong_mask,
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get_model, mean_abs_xcorr, oma_ser_keylen)
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from sse_lib import DATA, DEVICE, eval_ser_sse, write_csv
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SEED = 1
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def learned_model(d=MAIN_D, P=4, vu=16, U=4, iters=4000, seed=SEED):
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"""The learned counterpart of main_model: same everything, keys free."""
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return get_model(P=P, vu=vu, d=d, U=U, iters=iters, seed=seed)
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def keylen():
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"""Fig. 3's learned curve."""
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print("[learned] key length ...")
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rows = []
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for d in [32, 48, 64, 80, 96, 128, 192, 256]:
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m = learned_model(d=d)
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lg = eval_ser_sse(m, [10.0], frames=500_000)[0]
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ev = sum(eval_ser_eve(
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m, eve_wrong_mask(m.users, m.L,
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seed=20260813 + 101 * k).to(DEVICE),
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[10.0], frames=500_000 // 8)[0]
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for k in range(8)) / 8.0
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rows.append((m.L, d, lg, ev, mean_abs_xcorr(m.masks().detach()),
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oma_ser_keylen(m.L, 10.0)))
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print(" L=%3d legit %.4f eve %.4f" % (m.L, lg, ev))
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write_csv(DATA / "sec_keylen_learned.csv",
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["L", "d", "legit_ser", "eve_ser", "mask_xcorr", "oma"], rows)
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def jamming():
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"""Fig. 4's learned curves."""
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print("[learned] jamming ...")
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m = learned_model()
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jsr = [-10.0, -5.0, 0.0, 5.0, 10.0, 15.0, 20.0]
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blind = eval_ser_jam(m, 10.0, jsr, frames=500_000, mode="blind", target=0)
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matched = eval_ser_jam(m, 10.0, jsr, frames=500_000, mode="matched",
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target=0)
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nojam = eval_ser_jam(m, 10.0, [-40.0], frames=500_000, mode="blind",
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target=0)[0]
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write_csv(DATA / "sec_jam_learned.csv",
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["jsr_db", "blind", "matched", "nojam"],
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[(j, blind[i], matched[i], nojam) for i, j in enumerate(jsr)])
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print(" blind :", ["%.3f" % v for v in blind])
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def kpa():
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"""Fig. 7's learned curve. The attack is linear algebra on the key,
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so it applies to a real-valued key exactly as to a sign pattern."""
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print("[learned] known plaintext ...")
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m = learned_model()
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m.eval()
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true_m = m.masks().detach()
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nmax = max(exp_kpa.NFRAMES)
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rows = []
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for snr in exp_kpa.SNRS:
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acc = {n: [[], []] for n in exp_kpa.NFRAMES}
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for t in range(exp_kpa.TRIALS):
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gen = torch.Generator(device="cpu").manual_seed(
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exp_kpa.SEED + int(snr) + 1000 * t)
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digits, obs, h = exp_kpa.collect_known_plaintext(m, nmax, snr, gen)
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eval_seed = 777 + 31 * t + int(snr)
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for n in exp_kpa.NFRAMES:
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est = exp_kpa.solve_keys(m, digits[:n], obs[:n], h[:n])
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acc[n][0].append(exp_kpa.key_correlation(est, true_m))
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acc[n][1].append(eval_ser_eve(m, est.cpu(), [10.0],
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frames=exp_kpa.EVAL_FRAMES,
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seed=eval_seed)[0])
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for n in exp_kpa.NFRAMES:
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ks, ss = acc[n]
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rows.append((snr, n, sum(ks) / len(ks), sum(ss) / len(ss)))
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print(" %4.0f dB done" % snr)
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write_csv(DATA / "kpa_learned.csv",
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["snr_db", "n_frames", "kappa", "eve_ser"], rows)
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def refresh():
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"""Table VI's learned rows: the invariance refresh acts through
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eps^2 = 1 and a relabeling, so it is available to any real key."""
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print("[learned] refresh ...")
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m = learned_model()
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W0, B0 = m.W.detach().clone(), m.B.detach().clone()
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base = eval_ser_sse(m, [10.0], frames=300_000)[0]
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out = []
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for b in range(8):
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g = torch.Generator(device=DEVICE).manual_seed(5150 + b)
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xi = torch.randperm(m.L, generator=g, device=DEVICE)
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eps = torch.randint(2, (m.L,), generator=g, device=DEVICE) * 2.0 - 1.0
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tau = torch.randperm(m.users, generator=g, device=DEVICE)
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with torch.no_grad():
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m.W.copy_((W0[tau] * eps[None, :])[:, xi])
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m.B.copy_(B0[:, xi])
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lg = eval_ser_sse(m, [10.0], frames=300_000)[0]
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ev = eval_ser_eve(m, eve_wrong_mask(m.users, m.L,
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seed=20260813).to(DEVICE),
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[10.0], frames=300_000)[0]
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out.append((b, lg, ev))
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with torch.no_grad():
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m.W.copy_(W0); m.B.copy_(B0)
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write_csv(DATA / "refresh_learned.csv",
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["block", "legit_ser", "eve_ser"], out)
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print(" unrefreshed %.5f refreshed %.5f..%.5f"
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% (base, min(r[1] for r in out), max(r[1] for r in out)))
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def compare():
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"""Table IV's learned row: the same four columns as the structured
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scheme, under the same jammer at a JSR of 0 dB."""
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print("[learned] scheme comparison ...")
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m = learned_model()
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F = 300_000
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legit = eval_ser_sse(m, [10.0], frames=F)[0]
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out = eval_ser_eve(m, eve_wrong_mask(m.users, m.L,
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seed=20260813).to(DEVICE),
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[10.0], frames=F)[0]
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ins = eval_ser_eve(m, m.masks().detach().roll(1, 0), [10.0], frames=F)[0]
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jam = eval_ser_jam(m, 10.0, [0.0], frames=F, mode="blind", target=0)[0]
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write_csv(DATA / "compare_learned.csv",
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["scheme", "legit_ser", "eve_out", "eve_in", "jam0_ser"],
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[("proposed_learned", legit, out, ins, jam)])
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print(" legit %.4f out %.4f in %.4f jam %.4f"
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% (legit, out, ins, jam))
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def main():
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keylen()
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jamming()
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kpa()
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refresh()
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compare()
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print("[done] learned-key CSVs in", DATA)
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if __name__ == "__main__":
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main()
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