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Ship the learned-key pipeline, without which six figures cannot be rebuilt

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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.
This commit is contained in:
KiHoLee
2026-08-28 23:58:01 +09:00
parent ffe56b4b25
commit 138897aa8d
35 changed files with 853 additions and 168 deletions
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README.md
+28 -11
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@@ -32,6 +32,12 @@ code/
check_cov_*.py ciphertext-only covariance-attack checks (referee M1)
exp_real_sec.py stage G: real BERT WordPiece token streams
verify_math.py closed-form checks V1-V11, PASS/FAIL and verify_math.csv
exp_learned.py every KM (lrn.) artifact, one function per stage
run_learned_reg.py runs those stages in order, sens before brute
merge_learned_rows.py folds the learned rows into the two table sources
report_learned.py every learned number beside its structured one
diag_whygap.py why a fixed key beats a learned one here
diag_jscc.py where a learned mask would win instead
replot_security.py every result figure, from data/ to fig/
make_tables.py LaTeX rows of every result table, from data/
feasibility_security.py early CPU-sized study, kept for the record
@@ -60,10 +66,19 @@ python exp_users_csi.py # load and channel-estimate sweeps
python check_cov_attack.py # ciphertext-only covariance attack
python diag_maskdegen.py # learned-key support degeneracy
python check_family_enum.py # ciphertext-only enumeration of the key family
python run_learned_reg.py # every KM (lrn.) artifact, regularized keys
python merge_learned_rows.py # the learned rows of the two result tables
python replot_security.py # all figures from the CSVs
python make_tables.py # LaTeX rows of the result tables
```
The learned keys are the regularized ones of Section V-C, trained under
the two penalties rather than under the cross-entropy alone. Training on
the cross-entropy alone drifts to disjoint sparse supports, which is an
orthogonal slot allocation rather than a superposition; `diag_whygap.py`
measures that drift and `exp_learned.learned_model` says why the
regularized keys are the ones every figure draws.
Seeds are fixed: training 1, evaluation 777, attacker key guess
20260813, key recovery 4242, brute-force search 31, cross-scheme
comparison 11, key refresh 5150. Re-running reproduces the released CSV
@@ -82,17 +97,17 @@ Logarithms in an entropy or an information rate are base two.
| Artifact | Script | Data |
|---|---|---|
| Fig. 2 SER against SNR | `exp_full.stage_A` | `sec_snr.csv` |
| Fig. 3 key length | `exp_full.stage_B` | `sec_keylen.csv` |
| Fig. 4 jamming (4 schemes) | `exp_full.stage_C`, `stage_L` | `sec_jam_cmp.csv`, `sec_jam.csv` |
| Fig. 5 key sensitivity | `exp_full.stage_I` | `sec_sens_cmp.csv` |
| Fig. 6 brute-force search | `exp_full.stage_I`, `stage_F`, `stage_J` | `sec_brute_cmp.csv`, `sec_brute.csv` |
| Fig. 7 known-plaintext attack | `exp_kpa`, `exp_permkpa` | `kpa.csv`, `pkpa.csv` |
| Fig. 8 real token streams | `exp_real_sec` | `real_sec_ter.csv` |
| Scheme comparison table | `exp_full.stage_E` | `sec_compare.csv` |
| Key family table | `exp_full.stage_D` | `sec_maskfam.csv`, `sec_regjam.csv` |
| Headline recovery table | `exp_real_sec` | `real_sec_stats.json` |
| Key refresh tables | `exp_refresh` | `refresh_summary.csv`, `refresh_kpa.csv` |
| Fig. 2 SER against SNR | `exp_full.stage_A`, `stage_N` | `sec_snr.csv`, `sec_snr_learned.csv` |
| Fig. 3 key length | `exp_full.stage_B`, `exp_learned.keylen`, `.keylen_perm` | `sec_keylen.csv`, `sec_keylen_learned.csv`, `sec_keylen_perm.csv` |
| Fig. 4 jamming (5 curves) | `exp_full.stage_C`, `stage_L`, `exp_learned.jamming` | `sec_jam_cmp.csv`, `sec_jam.csv`, `sec_jam_learned.csv` |
| Fig. 5 key sensitivity | `exp_full.stage_I`, `exp_learned.sens` | `sec_sens_cmp.csv`, `sec_sens_learned.csv` |
| Fig. 6 brute-force search | `exp_full.stage_I`, `stage_F`, `stage_J`, `exp_learned.brute` | `sec_brute_cmp.csv`, `sec_brute.csv`, `sec_brute_learned.csv` |
| Fig. 7 known-plaintext attack | `exp_kpa`, `exp_permkpa`, `exp_learned.kpa` | `kpa.csv`, `pkpa.csv`, `kpa_learned.csv` |
| Fig. 8 real token streams | `exp_real_sec`, `exp_learned.real` | `real_sec_ter.csv`, `real_sec_ter_learned.csv` |
| Scheme comparison table | `exp_full.stage_E`, `exp_learned.compare`, `merge_learned_rows` | `sec_compare.csv` |
| Key families (Sec. VI-G prose) | `exp_full.stage_D` | `sec_maskfam.csv`, `sec_regjam.csv` |
| Headline recovery (Sec. VI-H prose) | `exp_real_sec` | `real_sec_stats.json` |
| Key refresh table | `exp_refresh`, `exp_learned.refresh`, `merge_learned_rows` | `refresh_summary.csv`, `refresh_kpa.csv` |
| Information-theoretic leakage | `exp_infotheory` | `infotheory.csv` |
| Semantic similarity | `exp_semantic` | `semantic.csv` |
| Load and channel-estimate sweeps | `exp_users_csi` | `users.csv`, `csi.csv` |
@@ -103,6 +118,8 @@ Run one stage on its own with `python code/exp_full.py stage_B`, or the whole ch
| Covariance attack (Sec. IV) | `check_cov_attack` | `cov_attack.csv` |
| Learned-key degeneracy (Sec. VI-F) | `diag_maskdegen` | `maskdegen.csv` |
| Closed-form and symbolic checks | `verify_math` | `verify_math.csv` |
| Why a fixed key wins here (not in the paper) | `diag_whygap` | `whygap.csv` |
| Where a learned mask would win (not in the paper) | `diag_jscc` | `jscc.csv` |
## Security scope