Users required 4.0–5.8 minutes on average to enhance a stegotext into natural language across three experiments, inserting 4–8 extra words per sentence; this is comparable to the time required to write a short email. The random-word-selection baseline consistently required more time and inserted more words, confirming that n-gram-guided word choice meaningfully reduces human editing burden.

Matryoshka achieves an average covert rate of ~3 bits/word after human enhancement; for a 5-word hidden message averaging 5.5 characters per word, the final enhanced stegotext is approximately 73 words. This is roughly 10× the covert rate of Spammimic (~0.3 bits/word), the prior leading approach.

§5 evaluation

After crowdsourced (MTurk) enhancement, 88% of stegotexts on average pass a One-Class SVM trained on 150K sentences from Wikipedia, Brown, and Reuters corpora as natural language; pre-enhancement, only 25–58% pass. For calibration, the same classifier correctly rejects 97% of randomly generated sentences as non-natural-language.

§5 evaluation

A mixed Huffman codebook combining character-level coding with explicit entries for the 300 most frequent English words (covering ~65% of written material) achieves a 52% compression ratio on average across 4,825 sentences of 4–15 words—7 percentage points better than a character-only alphabet—directly increasing the covert bits available per output word.

§3.1 defense

The Viterbi-based probabilistic decoder achieves zero character error rate on 96%, 93%, and 95% of decoded messages across the three corpora experiments (dreams, animals, facebook). For the small fraction of failures, only 15% of characters on average were corrupted rather than total message loss.

§5 evaluation

Stage 1 of the detection pipeline uses a lightweight heuristic: restrict analysis to IP addresses in "VPS-dense ASNs," which censors already target for resource-intensive inspection of fully-encrypted traffic. This pre-filter dramatically reduces the search space before applying the more expensive dual-role behavioral analysis. The evaluation was conducted without Stages 1 and 3 due to dataset limitations, meaning the reported 23% recall and 0.18% FPR are conservative lower bounds on the full pipeline's performance.

2026-almutairi-server §2.1, §2.2, §3.4 fully-encrypted-detectdpiml-classifier

NATA (Non-invasive Active Traffic-correlation Analysis) injects low-frequency bandwidth waveforms (sinusoidal, square-wave, triangular) into Tor TCP connections at an upstream gateway without endpoint compromise, payload decryption, or Tor-browser modification. BM-Net, a selective state-space classifier trained on the exit-side observations, achieves a 99.65% binary detection F1 score distinguishing watermarked from natural traffic on a 20,000-trace real-world dataset.

2026-fan-activeflowmark-assessing-tor §IV, §VI.D, Table III flow-correlationtraffic-shapeml-classifier

BM-Net achieves a 99.65% binary detection F1 score for distinguishing bandwidth-watermarked Tor flows from natural traffic, outperforming all evaluated baselines (next best: TikTok at 75.96% F1). The accuracy gap stems from active perturbation imposing a deterministic low-frequency throughput constraint rather than relying on subtle natural metadata, making the detection task fundamentally easier than passive website fingerprinting.

2026-fan-activeflowmark-assessing-tor §VI-D, Table III–IV flow-correlationtraffic-shapeml-classifier

BM-Net achieves a 99.65% binary detection F1 score distinguishing watermarked from natural Tor flows, and a 97.5% macro-F1 score for fine-grained modulation classification across sinusoidal, square-wave, and triangular patterns. The fine-grained test set contains 201 held-out samples collected from ten clients across five geographic regions (Europe, North America, Australia, Southeast Asia, East Asia), with training traces including traffic collected under WTF-PAD and Walkie-Talkie defenses.

2026-fan-activeflowmark-assessing-tor §VI-D, Table III, Table V flow-correlationml-classifiertraffic-shape

BM-Net achieves a 97.5% macro-F1 score for fine-grained classification of three modulation geometries (sinusoidal, square-wave, triangular) from noisy exit-side Tor observations using only 201 labeled test samples collected across cross-continental Tor paths. Residual errors concentrate between natural traffic and square-wave modulation, as abrupt low-rate transitions are partially smoothed by Tor multiplexing and network jitter.

2026-fan-activeflowmark-assessing-tor §VI-D, Table V flow-correlationml-classifiertraffic-shape

Fine-grained modulation classification (natural vs. sinusoidal vs. square-wave vs. triangular) achieves 97.5% macro-F1 on a 201-sample held-out test set. Square-wave waveforms are the hardest class (F1 = 95.7%), while sinusoidal and triangular each reach 99.0% F1, because abrupt square-wave transitions are partially smoothed by Tor multiplexing and network dynamics.

2026-fan-activeflowmark-assessing-tor §VI.D.2, Table V flow-correlationtraffic-shapeml-classifier