Under a strict FPR ≤ 0.1% operational budget, FlowPaint achieves a TPR of 0.00 against Decision Tree and nPrintML classifiers across multiple out-of-distribution protocols (CurveZMQ, secio, SSH, TLS), while Obfs4 and UPGen remain near-fully detectable (TPR ≈ 1.00). Even against Deep Fingerprinting, FlowPaint reduces TPR to 0.80–0.92, compared to 1.00 for Obfs4 across all protocols tested.

Under maximum-sensitivity classifier settings, FlowPaint forces Random Forest to accept ~25% FPR across all four out-of-distribution protocols in order to maintain high TPR — blocking 1 in 4 legitimate flows, a level of collateral damage operationally unsustainable for state-level censors. Obfs4 and UPGen exhibit clean separation (TPR ≈ 1.00, FPR ≈ 0.00), imposing no such dilemma on the censor and thus offering no protection from the censor's perspective either.

§5.1.2, Table 2 evaluation

FlowPaint evades all three GFW-style rule-based middlebox configurations — stateful TCP keyword reassembler (Censor A), heuristic header filter dropping flows with TCP data offset ≥ 9 (Censor B), and anti-replay validator caching (SEQ, payload_hash) pairs and injecting bidirectional RSTs on replay detection (Censor C). Geneva's duplicate-packet desynchronization strategy fails against Censor C because its malformed artifacts simultaneously satisfy the anti-replay blocking condition, while FlowPaint succeeds by recalculating checksums and injecting no extra packets.

§5.2, Table 4 defense

FlowPaint achieves a steady-state goodput of 3.05 Gbps versus 10.7–10.85 Gbps for lightweight baselines (UPGen, Dummy) that provide near-zero evasion (TPR ≈ 1.00), with an initial TTFB of ~1.9 s dominated by fixed diffusion model inference cost that grows by only 1.28× as network RTT increases tenfold (20 ms to 200 ms). At 3.05 Gbps, FlowPaint exceeds 4K and 8K streaming bandwidth requirements (25 Mbps and 100 Mbps respectively) by two orders of magnitude, indicating that compute overhead is a manageable hardware-scaling constraint rather than a fundamental barrier.

Appendix A, Table 7, Fig. 4 evaluation

Obfs4's payload randomization creates a high-entropy statistical profile that a Decision Tree detects with TPR ≈ 1.00 and FPR ≈ 0.00 across all four out-of-distribution protocols tested. UPGen's procedural protocol assembly similarly yields TPR ≈ 1.00 for most classifiers, confirming that both entropy randomization and rigid structural assembly leave artifacts detectable by even simple statistical classifiers operating under strict false-positive constraints.

§5.1.3, Table 2 detection

Based on analysis of over 100,000 leaked Geedge Networks documents, InterSecLab (2025) found that Geedge supplied China-GFW-equivalent censorship and surveillance systems to Kazakhstan, Ethiopia, Pakistan, and Myanmar. The systems include deep packet inspection, real-time mobile user monitoring, fine-grained traffic control, regionally customizable censorship rules, and — per WIRED's reporting — ML-based extraction of metadata from encrypted traffic to determine whether a flow is likely from a VPN or circumvention tool.

2026-anon-2026-27-vpn §8 (InterSecLab / WIRED section) dpiml-classifierfully-encrypted-detecttraffic-shape

Existing Provably Secure Steganography (PSS) schemes require access to the explicit output distribution of the generative model, a requirement that is impractical for black-box API deployments. This paper demonstrates that computational indistinguishability between normal and steganographic output can be achieved using only a seed-based model API, with no distribution access required.

2026-bai-provable-secure-steganography Abstract / §1 Introduction traffic-shapeml-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. Per-class F1 is 95.7% for square-wave, the hardest class, versus 99.0% each for sinusoidal and triangular.

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