Without traffic morphing, a χ² packet-length classifier can identify 90% of Facet (video-over-Skype) sessions with only a 10% false positive rate on genuine videoconferencing. To block 80% of Facet connections, the censor need only disrupt 4% of genuine Skype calls; blocking 70% requires disrupting only 2%.

Content inconsistency — transmitting non-native payloads (e.g., modem signals or general web traffic) over VBR-encoded VoIP/video channels — is sufficient for censors to detect camouflage systems via packet-length traffic analysis. Channel inconsistency — requiring reliable transport over a loss-tolerant UDP channel — allows selective disruption: dropping 5% of packets stalls SkypeMorph indefinitely, and dropping 90% for under one second desynchronizes the FreeWave modem.

§3.3 detection

A single Facet server with 15 Mbit/s bandwidth and 4 virtual CPU cores supports up to 20 simultaneous video-streaming sessions. Server-side downstream bandwidth consumption is approximately 150 kbit/s lower than a Squid HTTP proxy for equivalent video quality (240p), because Facet fetches only the video stream without page overhead such as advertisements and images.

§9.5, Table 3, Table 4 evaluation

For decentralized videoconferencing systems (e.g., Skype) where peers communicate directly, publicly distributing the Facet server's conferencing ID allows a censor to pinpoint the server's IP address via active probing. Centralized systems (e.g., Google Hangout, FaceTime) hide the proxy IP behind the provider's relay server, making active probing unable to identify the Facet server.

§5 (Service Discovery), §8 detection

Facet's video morphing — embedding the requested video in a fraction s of H.264 macroblocks within a randomly chosen chat video — raises the censor's required false positive rate dramatically. At steganography level s=0.125, blocking 90% of Facet connections requires disrupting over 40% of genuine videoconferencing traffic; blocking 80% requires disrupting at least 20% of legitimate calls.

§7.2, §9.3–9.4, Figure 9 defense

The host-profiling censor (passive traffic analysis: count connections per server, block those exceeding a threshold τ within a window w) blocks essentially all circumvention user traffic within 30 time steps for all classifier qualities tested (ρ_TP ∈ {0.9, 0.95, 0.99}), while causing far less collateral damage than zig-zag (never exceeding ~30% innocent server blocking). Snowflake resists this attack well: with w=3, τ=3, over 94.48% of users receive a proxy within 2 steps even with worst-classifier rules, and final unblocked server rates are 91.24–99.04%. The host profiling approach is feasible for passive censors who cannot enumerate the distribution channel.

2026-fares-game §5.2, Table 2, Table 3 traffic-shapeml-classifier

Balboa's synchronous leaf-content replacement adds non-negligible timing differences that allow censors to identify its activity with up to ~90% accuracy over different network conditions. The timing anomaly arises because Balboa performs data substitution directly at each data exchange, delaying the server's response while covert data is prepared.

2026-kamali-huma §I (Introduction), §II-A traffic-shapeml-classifier

Huma's deferred-reply / double-request receive (DRR) protocol reduces a traffic-fingerprinting XGBoost classifier's accuracy to at most 54% (near random guessing) across geographically distributed clients (San Francisco, Frankfurt, Bangalore). A Kolmogorov-Smirnov test on absolute page-load timing distributions yields D=0.03, p=0.98 for U.S. clients — substantially tighter than Waterfall of Liberty's D=0.11 at p=0.5 — confirming that Huma flows are statistically indistinguishable from benign HTTPS fetches.

2026-kamali-huma §V-D, Table II traffic-shapeml-classifier

The PPBR (probabilistic profile-based routing) protocol leaks user community membership through observable routing decisions: in a controlled experiment with 800 majority and 200 minority users, a statistical disclosure attack achieved a true positive rate of 100% and false positive rate of 0% when identifying minority users. Even under a conservative PPBR configuration (top 1/3 fraction acceptance), the attack achieved 100% TPR and only 0.4% FPR.

2026-ratliff-mirage §V ml-classifiertraffic-shape

Banking apps from major Russian institutions (Sber, T-Bank, VTB, Alfa-Bank) combine VPN detection with behavioral biometrics — screen pressure, touch coordinates, and gesture timing — enabling cross-account re-identification of users behind proxies. 11 apps received a "RED" (maximum surveillance) rating. T-Bank, Yandex services, and MAX additionally deploy active anti-analysis features that detect research tooling on the device (rooted devices, emulators, Frida, etc.).

2026-rks-russian-apps-vpn-detection §5, §6 ml-classifiertraffic-shape

Amnesty International's 102-page investigation identifies a multi-vendor surveillance stack deployed in Pakistan: Chinese DPI (Geedge/MESA-derived), Canadian social-media monitoring (Netsweeper), and Emirati commercial spyware (Pegasus and FinFisher). The system enables deep packet inspection, SNI-based filtering, and traffic-shape classification at national scale, including targeted interception of encrypted messaging apps and VPN traffic.

2025-amnesty-pakistan-shadows §3, §5 dpisni-blockingip-blockingtraffic-shapeml-classifier