KiHoLee
c00e8ab666
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.
160 lines
6.2 KiB
Python
160 lines
6.2 KiB
Python
# -*- coding: utf-8 -*-
|
|
"""Learned-key counterparts of the structured-key result stages.
|
|
|
|
Keyed masking is realized two ways, with structured Walsh-Hadamard keys
|
|
and with keys learned in R^L. The two differ in key space, so the paper
|
|
reports both wherever a figure or table carries a keyed-masking result.
|
|
This script produces the learned side of the key-length sweep, the
|
|
jamming sweep, the known-plaintext attack, the scheme comparison and
|
|
the refresh, writing files named *_learned.csv next to the structured
|
|
ones.
|
|
|
|
Every evaluation mirrors its structured counterpart exactly: same SNR,
|
|
same frame counts, same seeds, same evaluators. Only the key family
|
|
differs.
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import math
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
|
|
import exp_kpa
|
|
from exp_full import (MAIN_D, eval_ser_eve, eval_ser_jam, eve_wrong_mask,
|
|
get_model, mean_abs_xcorr, oma_ser_keylen)
|
|
from sse_lib import DATA, DEVICE, eval_ser_sse, write_csv
|
|
|
|
SEED = 1
|
|
|
|
|
|
def learned_model(d=MAIN_D, P=4, vu=16, U=4, iters=4000, seed=SEED):
|
|
"""The learned counterpart of main_model: same everything, keys free."""
|
|
return get_model(P=P, vu=vu, d=d, U=U, iters=iters, seed=seed)
|
|
|
|
|
|
def keylen():
|
|
"""Fig. 3's learned curve."""
|
|
print("[learned] key length ...")
|
|
rows = []
|
|
for d in [32, 48, 64, 80, 96, 128, 192, 256]:
|
|
m = learned_model(d=d)
|
|
lg = eval_ser_sse(m, [10.0], frames=500_000)[0]
|
|
ev = sum(eval_ser_eve(
|
|
m, eve_wrong_mask(m.users, m.L,
|
|
seed=20260813 + 101 * k).to(DEVICE),
|
|
[10.0], frames=500_000 // 8)[0]
|
|
for k in range(8)) / 8.0
|
|
rows.append((m.L, d, lg, ev, mean_abs_xcorr(m.masks().detach()),
|
|
oma_ser_keylen(m.L, 10.0)))
|
|
print(" L=%3d legit %.4f eve %.4f" % (m.L, lg, ev))
|
|
write_csv(DATA / "sec_keylen_learned.csv",
|
|
["L", "d", "legit_ser", "eve_ser", "mask_xcorr", "oma"], rows)
|
|
|
|
|
|
def jamming():
|
|
"""Fig. 4's learned curves."""
|
|
print("[learned] jamming ...")
|
|
m = learned_model()
|
|
jsr = [-10.0, -5.0, 0.0, 5.0, 10.0, 15.0, 20.0]
|
|
blind = eval_ser_jam(m, 10.0, jsr, frames=500_000, mode="blind", target=0)
|
|
matched = eval_ser_jam(m, 10.0, jsr, frames=500_000, mode="matched",
|
|
target=0)
|
|
nojam = eval_ser_jam(m, 10.0, [-40.0], frames=500_000, mode="blind",
|
|
target=0)[0]
|
|
write_csv(DATA / "sec_jam_learned.csv",
|
|
["jsr_db", "blind", "matched", "nojam"],
|
|
[(j, blind[i], matched[i], nojam) for i, j in enumerate(jsr)])
|
|
print(" blind :", ["%.3f" % v for v in blind])
|
|
|
|
|
|
def kpa():
|
|
"""Fig. 7's learned curve. The attack is linear algebra on the key,
|
|
so it applies to a real-valued key exactly as to a sign pattern."""
|
|
print("[learned] known plaintext ...")
|
|
m = learned_model()
|
|
m.eval()
|
|
true_m = m.masks().detach()
|
|
nmax = max(exp_kpa.NFRAMES)
|
|
rows = []
|
|
for snr in exp_kpa.SNRS:
|
|
acc = {n: [[], []] for n in exp_kpa.NFRAMES}
|
|
for t in range(exp_kpa.TRIALS):
|
|
gen = torch.Generator(device="cpu").manual_seed(
|
|
exp_kpa.SEED + int(snr) + 1000 * t)
|
|
digits, obs, h = exp_kpa.collect_known_plaintext(m, nmax, snr, gen)
|
|
eval_seed = 777 + 31 * t + int(snr)
|
|
for n in exp_kpa.NFRAMES:
|
|
est = exp_kpa.solve_keys(m, digits[:n], obs[:n], h[:n])
|
|
acc[n][0].append(exp_kpa.key_correlation(est, true_m))
|
|
acc[n][1].append(eval_ser_eve(m, est.cpu(), [10.0],
|
|
frames=exp_kpa.EVAL_FRAMES,
|
|
seed=eval_seed)[0])
|
|
for n in exp_kpa.NFRAMES:
|
|
ks, ss = acc[n]
|
|
rows.append((snr, n, sum(ks) / len(ks), sum(ss) / len(ss)))
|
|
print(" %4.0f dB done" % snr)
|
|
write_csv(DATA / "kpa_learned.csv",
|
|
["snr_db", "n_frames", "kappa", "eve_ser"], rows)
|
|
|
|
|
|
def refresh():
|
|
"""Table VI's learned rows: the invariance refresh acts through
|
|
eps^2 = 1 and a relabeling, so it is available to any real key."""
|
|
print("[learned] refresh ...")
|
|
m = learned_model()
|
|
W0, B0 = m.W.detach().clone(), m.B.detach().clone()
|
|
base = eval_ser_sse(m, [10.0], frames=300_000)[0]
|
|
out = []
|
|
for b in range(8):
|
|
g = torch.Generator(device=DEVICE).manual_seed(5150 + b)
|
|
xi = torch.randperm(m.L, generator=g, device=DEVICE)
|
|
eps = torch.randint(2, (m.L,), generator=g, device=DEVICE) * 2.0 - 1.0
|
|
tau = torch.randperm(m.users, generator=g, device=DEVICE)
|
|
with torch.no_grad():
|
|
m.W.copy_((W0[tau] * eps[None, :])[:, xi])
|
|
m.B.copy_(B0[:, xi])
|
|
lg = eval_ser_sse(m, [10.0], frames=300_000)[0]
|
|
ev = eval_ser_eve(m, eve_wrong_mask(m.users, m.L,
|
|
seed=20260813).to(DEVICE),
|
|
[10.0], frames=300_000)[0]
|
|
out.append((b, lg, ev))
|
|
with torch.no_grad():
|
|
m.W.copy_(W0); m.B.copy_(B0)
|
|
write_csv(DATA / "refresh_learned.csv",
|
|
["block", "legit_ser", "eve_ser"], out)
|
|
print(" unrefreshed %.5f refreshed %.5f..%.5f"
|
|
% (base, min(r[1] for r in out), max(r[1] for r in out)))
|
|
|
|
|
|
def compare():
|
|
"""Table IV's learned row: the same four columns as the structured
|
|
scheme, under the same jammer at a JSR of 0 dB."""
|
|
print("[learned] scheme comparison ...")
|
|
m = learned_model()
|
|
F = 300_000
|
|
legit = eval_ser_sse(m, [10.0], frames=F)[0]
|
|
out = eval_ser_eve(m, eve_wrong_mask(m.users, m.L,
|
|
seed=20260813).to(DEVICE),
|
|
[10.0], frames=F)[0]
|
|
ins = eval_ser_eve(m, m.masks().detach().roll(1, 0), [10.0], frames=F)[0]
|
|
jam = eval_ser_jam(m, 10.0, [0.0], frames=F, mode="blind", target=0)[0]
|
|
write_csv(DATA / "compare_learned.csv",
|
|
["scheme", "legit_ser", "eve_out", "eve_in", "jam0_ser"],
|
|
[("proposed_learned", legit, out, ins, jam)])
|
|
print(" legit %.4f out %.4f in %.4f jam %.4f"
|
|
% (legit, out, ins, jam))
|
|
|
|
|
|
def main():
|
|
keylen()
|
|
jamming()
|
|
kpa()
|
|
refresh()
|
|
compare()
|
|
print("[done] learned-key CSVs in", DATA)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|