KiHoLee
17d23fa76a
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it did not reproduce Figs. 4 and 6; the reproduction block was five scripts short; and the refresh numbers were from a superseded run (nearly three, 15.0 to 64.8 bits) against the manuscript's 2.3 and 23.8 to 364.6.
1.3 KiB
1.3 KiB
| 1 | check | claim | empirical | abs_err | tol | verdict |
|---|---|---|---|---|---|---|
| 2 | V1 legit self-alignment | 1.0 | 0.9994724071424732 | 0.000527592857526793 | 0.02 | PASS |
| 3 | V2a eve mean advantage | 0.0 | 0.0006469982936097643 | 0.0006469982936097643 | 0.003 | PASS |
| 4 | V2b eve SER @ 0dB | 0.99609375 | 0.995 | 0.0010937500000000044 | 0.015 | PASS |
| 5 | V2b eve SER @ 10dB | 0.99609375 | 0.9888333333333333 | 0.007260416666666658 | 0.015 | PASS |
| 6 | V2b eve SER @ 20dB | 0.99609375 | 0.9881666666666666 | 0.007927083333333362 | 0.015 | PASS |
| 7 | V2b eve SER @ 80dB | 0.99609375 | 0.9896666666666667 | 0.0064270833333333055 | 0.015 | PASS |
| 8 | V3a bias slope in kappa | 1.0 | 0.9932 | 0.0068 | 0.03 | PASS |
| 9 | V3b random mask E|corr| | 0.09973557010035818 | 0.10187042771408686 | 0.002134857613728683 | 0.002992067103010745 | PASS |
| 10 | V4a blind jammer projection mean | 0.0 | 0.00026396107284673695 | 0.00026396107284673695 | 0.003 | PASS |
| 11 | V4b blind jammer projection variance | 0.01558576233568703 | 0.015476787953278994 | 0.00010897438240803532 | 0.0001558576233568703 | PASS |
| 12 | V5 matched bias over blind RMS | 8.0 | 8.0 | 0.04 | 1.0 | PASS |
| 13 | V6 coded-OMA outage @ 10 dB | 0.0406 | 0.040575 | 0 | 0 | REFERENCE |
| 14 | V7 symbolic identities | exact | exact | 0 | 0 | PASS |
| 15 | V8 cross-period remainder | 0.0 | 0.000337 | 0.000337 | 0.0005 | PASS |
| 16 | V9 score-variance ratio | 2.8 | 2.8252 | 0.0252 | 0.05 | PASS |
| 17 | V10 format-matched OMA at 10 dB | 0.055 | 0.05520 | 0.00020 | 0.001 | PASS |
| 18 | V11 OMA closed form vs Monte Carlo | 0.081245 | 0.080925 | 0.0039 | 0.01 | PASS |