Appendix D - Concept-to-Code Map

Appendix D - Concept-to-Code Map#

Concept

File

Read first

Message encoding / length header

src/ns5_core.py

encode_string / decode_string

Image hash / seeds

src/ns5_core.py

derive_seed / get_image_hash

Deterministic permutation

src/ns5_core.py

permute_index

Hamming code / syndrome

src/ns5_core.py

build_hamming / syndrome

Matrix embedding

src/ns5_core.py

MatrixEmbedding._embed

Wet paper solver

src/ns5_core.py

solve_wet_paper / gauss_solve_GF2

Pixel-domain nsF5

src/ns5_core.py

nsF5Pixel._embed

JPEG-domain bridge

src/jpegstego.py

embed_jpeg / extract_jpeg

Compressed-domain nsF5 (decrement + wet paper)

yccstego/yccstego/nsf5.py

_embed_block

DCT and quantization tables

yccstego/yccstego/dct.py

_dct_matrix / scale_qtable

Experiment records & re-run

src/experiment.py

new_record / verify

High-level embed/extract

src/ns5_core.py

embed_string / extract_string

Chi-square statistics

src/steganalysis.py

chi2_stats / chi2_sf

RS statistics

src/steganalysis.py

rs_metrics

Combined verdict

src/steganalysis.py

analyze

v1 feature order

src/fsfeatures.py / ml_predict.py

FEAT_KEYS / V1_FEAT_KEYS

v2 143-D features

src/featurize_v2.py

ALL_FEATURE_NAMES / featurize_v2()

SRM preprocessing

src/srm_filter.py

srm_residuals_np / preprocess_batch_torch

Training pipeline

src/train_model.py

main()

Dataset generation

src/make_dataset.py

main()

C++ embedding wrapper

src/cppembed.py

embed_string / selfcheck

Dual-model inference

src/ml_predict.py

MLPredictor(model_path=…, clip_outliers=…)

GPU v1 features

gpu/featurize_gpu.py

batch operators / check_vs_cpu

GPU v2 features

gpu/featurize_v2_gpu.py

extract_features_v2_gpu() / self-check

GUI callbacks

src/gui.py

button events -> core functions