# Appendix E - Recommended Resources

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> [🌐 中文版](https://yukinoshita-lin.github.io/nsf5-steganography/zh/content/appE.html)




## Books

- Zhou Zhihua, Machine Learning (in Chinese) - especially Chapter 2 on model evaluation and selection;

- Hang Li, Statistical Learning Methods (in Chinese) - classic derivations of logistic regression and perceptrons;

- Gonzalez & Woods, Digital Image Processing - bit planes, histograms, and frequency-domain fundamentals (early experiments used its images; v1.4 data sources switched to campus photos and BOSSbase);

- Any Chinese textbook on information hiding and digital watermarking as broad reading.

## Core Papers (in reading order)

- Westfeld, F5 - A Steganographic Algorithm, 2001 - matrix embedding and F5;

- Westfeld & Pfitzmann, Attacks on Steganographic Systems, 1999 - chi-square test;

- Fridrich, Goljan & Du, Reliable Detection of LSB Steganography in Color and Grayscale Images, 2001 - RS analysis;

- Fridrich, Goljan, Lisonek & Soukal, Writing on Wet Paper, 2005 - wet paper coding;

- Pevny, Filler & Bas, Using High-Dimensional Image Statistics to Perform Unsigned Steganalysis - modern feature-based steganalysis (SPAM) entry point.

## Online

- The project README and the canonical results table `docs/RESULTS.md` (current numbers for the SRM / 143-D / 53-D experiments); GitHub: Yukinoshita-lin/nsf5-steganography;

- scikit-learn docs: LogisticRegression, GroupKFold, roc_curve;

- PyTorch tutorials (needed for the GPU pipeline);

- GitHub Actions documentation for CI/release.

> **Try it |** The handbook ends here. Return to the checklist in 0.4 and grade yourself item by item. If you can tick all of them, give yourself a certificate: nsF5 Steganography - From Zero to Understanding.