A document framed as a patent disclosure ("本发明", "the present invention") describes building a YouTube video-fingerprint database by collecting 1,000 videos across 10 categories from 5 collection sites, pairing MITM-decrypted plaintext video-chunk info (via mitmproxy) with the corresponding encrypted flow captured via tshark, yielding a dataset of 1,000 video fingerprints and 2,493 encrypted video flows; the authors then analyze fingerprint continuity in streamed (non-downloaded) encrypted flows, finding 698 of 2,493 flows (~28%) show missing or duplicated chunks against the fingerprint database.

MESA Lab researcher notes on an encrypted-video-identification project describe adding SSL-layer information output to more precisely trace a flow's true source/identity, and considering reinforcement learning so the identification model adapts as network conditions change, working within/around sapp's plugin limitations.

An internal system-design document for a "Web Fingerprint" module specifies a mirrored-traffic system that identifies specific web pages a monitored individual visits over encrypted connections (the worked example given is a specific politically-related YouTube channel homepage) and specific search-engine keywords typed into Google search (the worked example target keyword given is "FLG", i.e. Falun Gong), with accuracy targets of >=90% page-identification precision and >=95% keyword recall, feeding a downstream "reputation" scoring module. A companion Python implementation with a real "dataset_24_youtube_ fingerprints.csv" dataset performs the YouTube-page fingerprinting using picture-count and request-size-sequence features, and is explicitly scoped in its file path to a "特定开放通道" (a specific open/circumvention channel).

A patent draft (applicant not present in the extracted text, but topically and methodologically identical to MESA Lab's SAPP audio/video-identification plugin work in the same corpus) describes a content-level encrypted-video identification system: an SNI-triggered flow classifier groups downlink ACK-aligned packets into 'chunks', converts the chunk-size sequence into a long/short word-frequency signature, and matches it in O(1) against a fingerprint database built by actively crawling target sites through a MITM proxy. Tested on 1,000 YouTube videos captured from 5 locations, it reaches 96.19% accuracy needing only 6 online chunks at 3.33us per match, 90x+ faster than three prior published methods it benchmarks against.

A June 2024 internal MESA Team survey ('针对审查系统的科学研究及探测技术调研报告') catalogs the academic censorship-measurement toolkit (OONI, Augur, Satellite, Quack/Hyperquack, GFWatch, GFWeb, middlebox weaponization studies, traceroute-based middlebox localization, device fingerprinting) and separately reviews circumvention-tool countermeasures, explicitly naming Lantern alongside Psiphon, Tor Meek and Signal as tools using uTLS-style TLS ClientHello mimicry and domain-fronting.

An internal TSG operations/troubleshooting manual lays out TSG's full traffic pipeline (NIC -> mrzcpd/marsio capture driver -> sapp DPI engine -> firewall/proxy(KNI->TFE)/active-defense/WAN-NAT policy branches) and shows engineers using maat_redis_tool to pull the live Redis-synced blocking policy tables (TSG_SECURITY_COMPILE, TSG_OBJ_IP_ADDR, TSG_OBJ_APP_ID) and filter them by numeric policy/object ID to debug why a block rule isn't firing.

The internal 'MAAT网络流处理配置统一描述框架' engineering manual (v3.1.20, author 郑超, 2021) documents MAAT's Redis-synced rule-compilation framework and its RuleScan/Hyperscan-based pattern-matching engine (libmaatframe.so / librulescan), and records that RuleScan's fast-scan feature caused a production outage on 2019-03-19 and has been disabled ever since.