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Learned-key stages, per-scheme plot styles, KM naming

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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.
This commit is contained in:
KiHoLee
2026-08-28 20:15:45 +09:00
parent 3a9a5eebf4
commit c00e8ab666
17 changed files with 320 additions and 57 deletions
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code/replot_security.py
+75 -47
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@@ -56,27 +56,46 @@ plt.rcParams.update({
})
AXES_RECT = dict(left=0.215, right=0.970, top=0.955, bottom=0.225)
C_LEGIT = "#c0392b"
C_EVE = "#2c5fa8"
C_OMA = "#7f8c8d"
C_CH = "#95a5a6"
C_MATCH = "#8e44ad"
C_PUB = "#16a085"
C_LEARN = "#d98c00"
C_LEGIT = "#c0392b" # KM, structured keys
C_LEARN = "#d98c00" # KM, learned keys
C_OMA = "#7f8c8d" # orthogonal multiple access
C_PUB = "#16a085" # public masks
C_PERM = "#8e44ad" # permutation key
C_PAD = "#a0522d" # index cipher
C_EVE = "#2c5fa8" # an adversary of KM
C_CH = "#95a5a6" # chance and reference levels
C_MATCH = C_PUB # the matched jammer is what public masks admit
# One entry per curve the figures draw. Colour identifies the scheme and
# line style the role: solid for a legitimate rate, dashed for an
# adversary, dash-dot for a comparison scheme, dotted for a reference.
# Every figure reads its curves from here, so a reader who learns a
# curve in one figure reads the same curve in the next.
STY = {
"km_str": dict(color=C_LEGIT, marker="o", ls="-"),
"km_lrn": dict(color=C_LEARN, marker="d", ls="-"),
"oma": dict(color=C_OMA, marker="^", ls=":"),
"pub": dict(color=C_PUB, marker="v", ls="-."),
"perm": dict(color=C_PERM, marker="X", ls="--"),
"pad": dict(color=C_PAD, marker="P", ls="-."),
"eve": dict(color=C_EVE, marker="s", ls="--"),
"insider": dict(color=C_EVE, marker="v", ls="-."),
}
# fixed label dictionary: tables and prose copy these strings verbatim
LBL = {
"legit": "Legitimate",
"legit_learned": "Learned keys",
"legit": "KM (structured)",
"legit_learned": "KM (learned)",
"oma": "OMA",
"eve_pub": "Eavesdropper, public masks",
"eve_key": "Eavesdropper", # the wrong-key condition is in the caption
"chance": "Random guess",
"nojam": "No jammer",
"mask": "Keyed masking",
"mask": "KM (structured)",
"perm": "Permutation key",
"pad": "Index cipher",
"insider": "Insider",
"legit_ref": "Legitimate rate",
"outsider": "Outsider",
}
# deliberate-layering style for the LOWER of two coinciding curves
@@ -207,7 +226,7 @@ def main_legit(snr_db="10"):
def place_legend(ax, cands=("lower left", "upper left", "center left",
"center right", "lower center", "upper right",
"upper center", "center", "lower right"),
sizes=(9.2, 8.8, 8.4, 8.0, 7.6, 7.2), ncol=1):
sizes=(9.2, 8.8, 8.4, 8.0, 7.6, 7.2, 6.8, 6.4), ncol=1):
"""Choose the location and font size whose box the fewest curve points
fall inside, scored on rendered geometry rather than guessed from the
data. The size sweep is what makes a long label set placeable: a
@@ -271,23 +290,21 @@ def fig_snr():
# legitimate and the public-mask eavesdropper coincide by
# construction (same physical layer, public masks decode alike), so
# the pair is deliberately layered; OMA is separate at this frame
ax.semilogy(x, col(r, "legit"), color=C_LEGIT, marker="o", ls="-",
ax.semilogy(x, col(r, "legit"), **STY["km_str"],
markevery=(0, 3), label=LBL["legit"], **UNDER)
# the learned family is the other end of the key-space trade-off,
# so the figure carries what it costs at every SNR
rl = load("sec_snr_learned.csv")
ax.semilogy(col(rl, "snr_db"), col(rl, "legit"), color=C_LEARN,
marker="d", ls="-", markevery=(2, 3),
label=LBL["legit_learned"])
ax.semilogy(x, col(r, "oma"), color=C_OMA, marker="^", ls=":",
ax.semilogy(col(rl, "snr_db"), col(rl, "legit"), **STY["km_lrn"],
markevery=(2, 3), label=LBL["legit_learned"])
ax.semilogy(x, col(r, "oma"), **STY["oma"],
markevery=(1, 3), label=LBL["oma"], **OVER)
ax.semilogy(x, col(r, "eve_public"), color=C_PUB, marker="v",
ax.semilogy(x, col(r, "eve_public"), color=STY["pub"]["color"], marker=STY["pub"]["marker"],
ls="none", markevery=(2, 3), markerfacecolor="none",
label=LBL["eve_pub"])
# this figure carries two eavesdroppers, so the bare label of the
# key-length figure would not tell them apart
ax.semilogy(x, col(r, "eve_wrong"), color=C_EVE, marker="s", ls="--",
label="Eavesdropper, keyed")
ax.semilogy(x, col(r, "eve_wrong"), **STY["eve"], label="Eavesdropper, keyed")
# the chance level lies within 3.5e-4 of the wrong-key curve, so it is
# drawn for reference but left out of the legend, which the caption
# names instead; five long entries leave this figure no clear corner
@@ -295,10 +312,9 @@ def fig_snr():
ax.set_xlabel("SNR (dB)")
ax.set_ylabel("SER")
ax.set_xlim(min(x), max(x))
# the five-entry legend needs more clear space than the four-entry
# one did, so the axis opens a further decade below the data; the
# lower-left is empty because every curve decays
ax.set_ylim(bottom=2e-5)
# most of a decade below the data leaves the lower-left genuinely
# empty, which is what gives the legend a clear berth
ax.set_ylim(bottom=2e-4)
place_legend(ax)
save(fig, "fig_sec_snr")
@@ -310,14 +326,16 @@ def fig_keylen():
r = load("sec_keylen.csv")
x = col(r, "L", int)
fig, ax = plt.subplots()
ax.semilogy(x, col(r, "legit_ser"), color=C_LEGIT, marker="o", ls="-",
label=LBL["legit"])
ax.semilogy(x, col(r, "legit_ser"), **STY["km_str"], label=LBL["legit"])
rl = load("sec_keylen_learned.csv")
ax.semilogy(col(rl, "L", int), col(rl, "legit_ser"), **STY["km_lrn"], label=LBL["legit_learned"])
op = [(l, v) for l, v in zip(x, col(r, "oma")) if not math.isnan(v)]
ax.semilogy([p[0] for p in op], [p[1] for p in op], color=C_OMA,
marker="^", ls=":", label=LBL["oma"])
ax.semilogy(x, col(r, "eve_ser"), color=C_EVE, marker="s", ls="--",
label=LBL["eve_key"])
ax.semilogy([p[0] for p in op], [p[1] for p in op], **STY["oma"], label=LBL["oma"])
ax.semilogy(x, col(r, "eve_ser"), **STY["eve"], label=LBL["eve_key"])
ax.set_ylim(top=22.0) # headroom above the flat eavesdropper curve
# an error rate cannot exceed one, and the room below the data holds
# the legend, since every curve decays to the right
ax.set_ylim(top=1.4, bottom=1.2e-2)
ax.set_xlabel("Key length $L$")
ax.set_ylabel("SER")
ax.set_xscale("log", base=2)
@@ -335,14 +353,17 @@ def fig_jam():
x = col(r, "jsr_db")
me = max(1, len(x) // 8)
fig, ax = plt.subplots()
ax.plot(x, col(r, "matched"), color=C_MATCH, marker="P", ls="--",
rj = load("sec_jam_learned.csv")
ax.plot(col(rj, "jsr_db"), col(rj, "blind"), **STY["km_lrn"],
markevery=(1, me), label=LBL["legit_learned"])
ax.plot(x, col(r, "matched"), **STY["pub"],
markevery=me, label="Public masks")
ax.plot(x, col(r, "oma_targeted"), color=C_PUB, marker="^", ls=":",
ax.plot(x, col(r, "oma_targeted"), **STY["oma"],
markevery=me, label=LBL["oma"])
# the two blind curves agree to 0.002; deliberate layering
ax.plot(x, col(r, "blind"), color=C_LEGIT, marker="o", ls="-",
ax.plot(x, col(r, "blind"), **STY["km_str"],
markevery=(0, me), label=LBL["mask"], **UNDER)
ax.plot(x, col(r, "perm_blind"), color=C_EVE, marker="s", ls="-.",
ax.plot(x, col(r, "perm_blind"), **STY["perm"],
markevery=(me // 2, me), label=LBL["perm"], **OVER)
nojam = float(load("sec_jam.csv")[0]["nojam"])
# the unjammed reference is named in the caption rather than in the
@@ -367,11 +388,11 @@ def fig_sens():
r = load("sec_sens_cmp.csv")
x = col(r, "frac")
fig, ax = plt.subplots()
ax.plot(x, col(r, "ser_mask"), color=C_LEGIT, marker="o", ls="-",
ax.plot(x, col(r, "ser_mask"), **STY["km_str"],
markevery=(0, 3), label=LBL["mask"], **UNDER)
ax.plot(x, col(r, "ser_perm"), color=C_EVE, marker="s", ls="--",
ax.plot(x, col(r, "ser_perm"), **STY["perm"],
markevery=(1, 3), label=LBL["perm"], **OVER)
ax.plot(x, col(r, "ser_pad"), color=C_PUB, marker="v", ls="-.",
ax.plot(x, col(r, "ser_pad"), **STY["pad"],
markevery=(2, 3), lw=1.2, mfc="none", label=LBL["pad"])
# the chance level comes from the stored curve, not from a second
# copy of the configuration constants
@@ -379,7 +400,7 @@ def fig_sens():
ax.axhline(chance, color=C_CH, ls=":", lw=0.9, label=LBL["chance"])
# the narration reads these curves against the legitimate rate
ax.axhline(main_legit(), color=C_OMA, ls=(0, (4, 2)), lw=0.9,
label=LBL["legit"])
label=LBL["legit_ref"])
ax.set_xlabel("Fraction of the key recovered")
ax.set_ylabel("Eavesdropper SER")
ax.set_xlim(0, 1)
@@ -393,15 +414,15 @@ def fig_brute():
r = load("sec_brute_cmp.csv")
x = col(r, "K")
fig, ax = plt.subplots()
ax.semilogx(x, col(r, "ser_perm"), color=C_EVE, marker="s", ls="--",
ax.semilogx(x, col(r, "ser_perm"), **STY["perm"],
markevery=(0, 3), label=LBL["perm"], **UNDER)
ax.semilogx(x, col(r, "ser_pad"), color=C_PUB, marker="v", ls="-.",
ax.semilogx(x, col(r, "ser_pad"), **STY["pad"],
markevery=(1, 3), label=LBL["pad"], **OVER)
ax.semilogx(x, col(r, "ser_mask"), color=C_LEGIT, marker="o", ls="-",
ax.semilogx(x, col(r, "ser_mask"), **STY["km_str"],
markevery=(2, 3), label=LBL["mask"])
legit = main_legit()
ax.axhline(legit, color=C_OMA, ls=(0, (4, 2)), lw=0.9,
label=LBL["legit"])
label=LBL["legit_ref"])
ax.set_xlabel("Number of key guesses $K$")
ax.set_ylabel("Eavesdropper SER")
ax.set_ylim(0.0, 1.05) # keep the reference line off the spine
@@ -415,13 +436,13 @@ def fig_real():
fig, ax = plt.subplots()
# insider and outsider still nearly coincide and are layered; the
# legitimate and OMA curves are separate at this frame
ax.semilogy(x, col(r, "ter_legit"), color=C_LEGIT, marker="o", ls="-",
ax.semilogy(x, col(r, "ter_legit"), **STY["km_str"],
markevery=(0, 2), label=LBL["legit"], **UNDER)
ax.semilogy(x, col(r, "ter_oma"), color=C_OMA, marker="^", ls=":",
ax.semilogy(x, col(r, "ter_oma"), **STY["oma"],
markevery=(1, 2), label=LBL["oma"], **OVER)
ax.semilogy(x, col(r, "ter_insider"), color=C_PUB, marker="v", ls="-.",
ax.semilogy(x, col(r, "ter_insider"), **STY["insider"],
markevery=(0, 2), label=LBL["insider"], **UNDER)
ax.semilogy(x, col(r, "ter_eve"), color=C_EVE, marker="s", ls="--",
ax.semilogy(x, col(r, "ter_eve"), **STY["eve"],
markevery=(1, 2), label=LBL["outsider"], **OVER)
ax.set_xlabel("SNR (dB)")
ax.set_ylabel("TER")
@@ -444,7 +465,14 @@ def fig_kpa():
ser = [float(row["eve_ser"]) for row in rows]
ax.semilogx(n, ser, color=c, marker=mk, ls="-",
markevery=(off, 4), markerfacecolor="none" if off else c,
label=LBL["mask"] + f", {int(snr)} dB")
label=f"KM (str.), {int(snr)} dB")
if snr == 10.0:
kl = [q for q in load("kpa_learned.csv")
if float(q["snr_db"]) == snr]
ax.semilogx([float(q["n_frames"]) for q in kl],
[float(q["eve_ser"]) for q in kl], **STY["km_lrn"],
markevery=(2, 4),
label=f"KM (lrn.), {int(snr)} dB")
try:
p = load("pkpa.csv")
ax.semilogx(col(p, "n_frames"), col(p, "eve_ser"), color=C_MATCH,
@@ -458,7 +486,7 @@ def fig_kpa():
# scheme-comparison table
legit = main_legit()
ax.axhline(legit, color=C_OMA, ls=(0, (4, 2)), lw=0.9,
label=LBL["legit"])
label=LBL["legit_ref"])
ax.set_xlabel("Known-plaintext frames $N$")
ax.set_ylabel("Eavesdropper SER")
ax.set_xscale("log", base=2)