The package was missing every script behind the KM (lrn.) curves and
both learned table rows: exp_learned's driver, the merge that folds the
learned rows into sec_compare.csv and refresh_summary.csv, and the
report that reads the learned numbers back. It was also missing
sec_keylen_perm.csv, so Fig. 3 could not be regenerated at all, and the
two diagnostics that answer why a fixed key beats a learned one here and
where a learned mask would win instead.
The learned artifacts themselves are regenerated. They were trained on
the cross-entropy alone, which drifts to disjoint sparse supports: 99
percent of each key's energy on about six of the 64 entries, so a digit
is decided over a sixth of its period and the key set is a choice of
support rather than a dense direction in R^L. They are now the
regularized keys of Section V-C, and check_consistency asserts which of
the two families the figures draw.
Verified from inside this repository: replot_security.py rebuilds all
seven result figures, make_tables.py reproduces both result tables, and
check_consistency.py passes every check that does not need the
manuscript.
The README now lists what ships. Its run list, layout and figure map had
none of the learned pipeline, named two tables the manuscript renders as
prose, and gave Fig. 3 no data file for its permutation curve.
check_consistency.py asserts that a million random guesses leave the
learned key at 0.72, and that the learned key falls to known plaintext
the way the structured one does, since the solve is linear in the key and
indifferent to whether its unknowns are signs or reals.
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.
check_family_enum.py now runs both attacks against both families. The
outsider ranks the L-1 Walsh rows; the insider, holding m_v, ranks the
L-1 products m_v .* m_r, which works because Walsh rows are closed
under the elementwise product and the per-block sign cancels in
m_u .* m_v. Both need a list to rank, and only the structured family
supplies one: the structured family falls at 0.905 from one frame at
10 dB and 0.990 from four, the refresh takes the outsider to 0.000 and
leaves the insider at 0.980, and the learned family gives 0.000
throughout.
exp_full.stage_N sweeps the learned family over the same SNR grid at
the same frame count as stage_A, so Fig. 2 can carry both families and
a reader can see what the key space costs at every SNR rather than at
one point.
check_consistency.py gains four assertions for the key-space
measurements and two for the learned sweep, 82 in all.
README: the assertion count was two rounds stale, and the figure map
omitted family_enum, cov_attack and maskdegen, whose CSVs back quoted
manuscript numbers.
check_family_enum.py measures the attack the manuscript now states in
Section III-A: the winning correlation is an index-free verifier, so
ranking the 63 non-constant Walsh rows by mean winning correlation
recovers the user set from one frame in 0.905 of 200 trials at 10 dB
and from four frames in 0.990, using nothing outside the stated threat
model. Under the invariance refresh it recovers it in none, because the
entry permutation relabels the codebook the adversary must align
against.
V8 and V9 read the trained codebook through main_model(), which
retrains on every call, and a codebook trained on CUDA is not the one
trained on CPU. The shipped verify_math.csv therefore read PASS here
and FAIL for anyone running this package without a GPU. model_main.pt
is 7 KB and fixes the codebook, which is what both checks are about;
delete it to retrain. V1-V11 now pass on both.
New checks: V10, the format-matched OMA reference Section VI-B quotes,
and V11, the closed-form against Monte Carlo comparison the manuscript
claimed and never stored. V3a's bias-linearity result was computed and
printed but never written to the CSV, so the one linearity claim the
paper quotes was the one this package could not show.
check_consistency.py gains 21 assertions, covering five data files that
no assertion read (users, csi, semantic, cov_attack, sec_jam) and the
trend claims it structurally could not see, since it compared values
and not shapes.
README: the figure map named stages that do not write the artifacts
they list, so following it did not reproduce Figs. 4 and 6; the
reproduction block was five scripts short; and the refresh numbers were
from a superseded run (nearly three, 15.0 to 64.8 bits) against the
manuscript's 2.3 and 23.8 to 364.6.
The jammed OMA model now matches the transmit chain the paper
describes, the key-length sweep averages the eavesdropper over eight
substitute-key draws, and verify_math gains the coded-OMA outage
reference, symbolic checks of the three algebraic identities, and the
cross-period remainder of Proposition 2. The README records the energy
and channel conventions.
diag_maskdegen writes data/maskdegen.csv so the claim it supports is
traceable; the legend guard inflates by the marker radius and refuses a
legend that leaves the canvas; one legend size on every figure.
Every OMA reference takes the L/16 combining gain so the comparison
stays resource matched, four hardcoded copies of the configuration are
replaced by MAIN_D or the main curve, and stage_J's K-by-L Gaussian
draw becomes its exact scalar Beta equivalent.
check_texhealth.py flags control characters and bare macro stubs left
by a shell heredoc, a class of corruption that LaTeX compiles without
complaint. It skips when main.tex is absent, as in this package.
fig_sec_snr now carries an inset with the OMA-to-proposed SER ratio,
since the 1 to 9 percent advantage is invisible across two decades of
log axis, and save() now guards inset overlap as it guards the legend.
check_consistency covers the L=8 crossover, the inset ratio span, the
stated secret sizes, and the Fig. 5 curve coincidence: 29 assertions.
Unconstrained key training converged to disjoint sparse supports: 99
percent of each users key energy sat on three or four of the sixteen
entries, with pairwise disjoint supports and one numerically dead
codebook column. That is an orthogonal slot allocation, so the
superposition collapsed into OMA and the key space was far smaller than
the dense direction the brute-force study assumes.
The main configuration is now the structured Walsh-Hadamard family,
which is dense, exactly orthogonal, unit modulus, and already the best
family in the key-family table. base_keys generalizes to any key length
by truncating the next power-of-two Sylvester order, and the key-length
sweep keeps only lengths where the truncated rows stay exactly
orthogonal, verified numerically.
Also fixes the M-PAM energy normalization in oma_ser_keylen, which used
sqrt(6g/(M^2-1)) where unit average symbol energy gives A^2=3/(M^2-1);
the closed form was 3 dB optimistic and now reproduces a direct Monte
Carlo to 1e-5.
Results move accordingly: the proposal now stays below OMA at every SNR
and reaches 1.52x at key length 64, while the jamming margin falls to
5.5-6.3 dB and the brute-force curve to 0.59 at a million guesses.
check_consistency.py asserts every headline number against its raw CSV,
so a stale quoted value fails loudly instead of surviving a revision. It
skips the manuscript-side assertions when main.tex is absent, which is
the case in this package.
The blind-versus-permutation agreement is 0.002, not 0.0015, and the
resource-matched OMA is undefined below L=16 rather than wherever 16/L
is not an integer.
KiHoLee