Current randomized-payload circumvention tools (obfs4/ScrambleSuit, SkypeMorph, VoIP-tunneling) rely on censors 'defaulting open' — treating unidentified traffic as innocuous. If censors instead block all traffic not explicitly recognizable as meaningful plaintext, these tools fail entirely. The paper notes anecdotal evidence this is already occurring, including blocking of some TLS 1.3 connections.

Variable-length sampling (Adaptation 2) achieves a provably secure but impractical encoding: a 16-byte plaintext encoded with GPT-2 requires 502–2994 tokens, produces 2.3–13.6 KiB of stegotext (149×–870× overhead), and takes 42–765 seconds even with GPU acceleration, depending on security parameter k=16–128.

§4 / Table 1 evaluation

Classical public-key steganography (Algorithm 1 from [54]) has a 100% failure rate when encoding a 16-byte message using GPT-2, because GPT-2's per-token entropy drops near zero frequently and standard rejection sampling cannot find an acceptable token. Entropy bounding reduces failure to 0–10% but introduces detectable statistical bias: selected tokens come from a visibly different probability distribution than baseline samples.

§4 Adapting Classical Steganographic Schemes / Figure 2b evaluation

Meteor encodes bits by embedding a PRG-masked random value into the token-sampling randomness of a generative model, recovering bits proportional to the shared prefix length of the sampled interval. Expected throughput per sampling event is asymptotically within 1/2 of the Shannon entropy of the channel (proven in Appendix A), so Meteor automatically adapts to high entropy variability without explicit signaling or padding.

§5 Meteor / §5.2 defense

Meteor is proven secure against chosen-hiddentext attacks: any PPT adversary distinguishing Meteor output from honest model output can be reduced to breaking the underlying PRG. The scheme produces stegotext provably indistinguishable from the generative model's own output distribution, and requires only a shared public model — not a secret channel — making the model analogous to a common random string. On GPU the encoding overhead is ~1× model-load time; on CPU ~4.6×; on mobile ~49.5×.

§5.2 / §6 / Table 2 defense

The GFW detects Shadowsocks by flagging apparently high-entropy connections that are not TLS or HTTP, but this detection is brittle: connections are explicitly allowed if the first 6 bytes of the first packet of a flow are all printable ASCII characters (range 0x20–0x7E). Adding a 6-byte alphanumeric preamble to the Shadowsocks message definition is sufficient to bypass this heuristic and requires only a short patch to the protocol specification file.

2023-wails-proteus §3.2 random-payload-detectfully-encrypted-detectdpi

The GFW's fully-encrypted detector (deployed Nov 2021) operates by exempting likely-benign traffic and blocking the rest. Five inferred exemption rules applied to the first TCP payload (pkt): Ex1 — popcount(pkt)/len(pkt) ≤ 3.4 or ≥ 4.6 (bits/byte); Ex2 — first 6+ bytes are printable ASCII [0x20–0x7e]; Ex3 — more than 50% of bytes are printable ASCII; Ex4 — more than 20 contiguous printable ASCII bytes; Ex5 — first bytes match TLS or HTTP fingerprint. Traffic failing all five exemptions is blocked. Experiments confirmed all rules still held as of February 2023.

2023-wu-fully-encrypted-detect §4, Algorithm 1 fully-encrypted-detectdpirandom-payload-detect

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

Two Iranian ASes apply a protocol allowlist that drops traffic not matching known application-layer protocol patterns (after ~6 packets), independently of the destination IP. Experiments with fresh /26 phantom subnets showed that prefixing Conjure connections with a plain HTTP GET payload evaded this blocking for four weeks, while TLS Client Hello-prefixed and SSH-prefixed connections were blocked within 72 hours (TLS) or 72 hours after port rotation (SSH). HTTP GET on port 80 was the only tested prefix that survived the full experiment window.

2025-alaraj-iran-refraction §4.4, §5 dpifully-encrypted-detect

Iran's June 2025 shutdown enforced a strict national protocol whitelist: only DNS (UDP/53), HTTP (port 80), and HTTPS (port 443) traffic from Iranian networks to external servers was forwarded; all other protocols—including OpenVPN (UDP/1194), SSH (port 22), and arbitrary TCP/UDP ports—were silently dropped without response by DPI at the border.

2025-aryapour-stealth-blackout §4.4 dpiport-blockingfully-encrypted-detect

The September 2025 leak of ~600 GB from Geedge Networks and the MESA Lab (Institute of Information Engineering, Chinese Academy of Sciences) is the largest known document disclosure from the GFW vendor ecosystem. It establishes a direct lineage: MESA Lab (founded 2012 by Fang Binxing's team, annual contracted revenue >35M RMB by 2016) spun out Geedge Networks in 2018, with MESA alumni filling key engineering roles (e.g. Zheng Chao as CTO). The leak includes ~64 GB of MESA git repositories, ~35 GB of MESA internal documents, ~15 GB of Geedge internal documents, and a ~3 GB Jira export — providing direct access to source code, work logs, and internal communications behind GFW R&D.

2025-geedge-mesa-leak §4 dpiactive-probingml-classifiersni-blockingfully-encrypted-detect