Message In A Bottle: Sailing Past Censorship

Over 72 days, the authors observed 814,667,299 blog posts (average 11,314,823/day; peak 13,083,878/day). To blacklist all potential MIAB drop points, a censor would need to block 33,361,754 FQDNs (5% of all web servers per Netcraft) or 1,803,345 second-level domains (1.4% of global domain registrations); even a fully-maintained static blacklist retains an 11–12% daily miss ratio as new blogs appear.

§5.1, Figure 4 evaluation ip-blocking

Existing censorship-resistant systems share a fundamental vulnerability: they require the user to know a finite set of entry points (bridge addresses, rendezvous points, or ISP-level collaborators) that a censor can enumerate by impersonating a legitimate user. China has blocked the majority of Tor bridges since 2010 and Iran blocked all encrypted traffic in 2012, demonstrating this attack is operationally deployed at scale.

§1 detection active-probingip-blocking cnir

MIAB reduces the bootstrap requirement to only the operator's public key — no pre-shared rendezvous point is needed — by using blog pings as a real-time broadcast discovery channel. Since every blog post on the Internet is a potential drop point, the censor cannot enumerate entry points by posing as a legitimate user, unlike Collage (requires an up-to-date task database) or Telex (requires ISP collaboration).

§3 defense ip-blockingactive-probing

A single modern machine with a fast domestic Internet connection can process the full blog-ping stream within the 5-minute ping-server release interval: steganographic extraction takes 2m:51s, RSA decryption 2m:35s, and image fetching 4m:17s (parallelizable with extraction), completing under 5 minutes at under 90% CPU. A single machine accommodates 15–20 million posts per day; serving Iran's entire population blogging daily would require only five machines.

§5.1 evaluation

All trained ML classifiers (K-NN, Naive Bayes, ANN, SVM, vote ensemble) performed at near-chance levels when distinguishing RSA-encrypted stego messages from clean photos — best precision 52.05%, best meaningful recall 61.52% (K-NN on clean class). The authors attribute this to embedding only a few hundred bytes into cover photos hundreds of KB in size, with natural image entropy in noisy pixel regions being empirically indistinguishable from RSA-ciphertext statistics.

§5.1, Table 1 defense ml-classifierrandom-payload-detect