How Does China Use AI in Intelligence

Relying on the “smart policing” system, China’s Ministry of Public Security used AI to assist in solving 350,000 cases in 2023. The Skynet project deployed more than 500 million cameras to achieve dynamic tracking, and built a “honeycomb” platform to process 10PB of multimodal data per day, combining voiceprint recognition and network behavior analysis to strengthen intelligence analysis.

AI Instantly Parses Massive Intelligence

Last week, a dark web forum suddenly leaked a 27TB data package containing encrypted communication records from the Ukrainian border and oil transportation route maps of a certain country. Bellingcat’s verification matrix showed that 12% of satellite image timestamps had UTC offsets exceeding 3 seconds compared to ground monitoring – normally this might be considered technical glitches, but during warship movements in the South China Sea disputed area, it directly triggered the geopolitical redline early warning model. A Docker image from a domestic intelligence laboratory revealed they used spatiotemporal hashing algorithms for three-layer cross-verification of satellite images, ship AIS signals, and Twitter keywords. Imagine stitching footage from 20 surveillance cameras across different time zones into a real-time combat map. Mandiant’s 2023 EM-271 report mentioned similar technology reduced false positive rates from 37% to under 8% when identifying stealth drones of a certain country.

When encountering sudden “fishing boat attack” videos in Telegram channels, the AI first does three things: Check video metadata GPS altitude data (shouldn’t show 83m elevation for sea shots), compare cloud movement patterns (use Sentinel-2 satellite cloud maps to reverse-engineer shooting time), and detect perplexity scores of local dialect language models (if ppl value suddenly jumps from 72 to 89, it’s likely machine-generated fake news). A forged border conflict incident last year was pinpointed within 23 minutes using these three indicators.

• ▎January 2024: An encrypted channel (linked to MITRE T1589.002) suddenly received 170,000 messages, with language models detecting 23x baseline frequency spikes for keywords “port”+”blackout”
• ▎When a dark web forum’s data volume exceeded 2.4TB, Tor exit nodes’ fingerprint collision rate jumped from 6% to 19%, triggering data pollution alerts

A typical operational case: Last year’s “missing fishing boat” rumor had three contradictory signals captured by AI systems at UTC 2023-11-17T08:47:32Z – the boat’s final AIS speed before disappearance was 28 knots (far exceeding normal), nearby weather radar showed wave heights below 1 meter, while a Twitter account uploaded “giant wave sinking” video simultaneously. Through multispectral satellite image overlay analysis (patent CN202310298888.3), the fabrication was confirmed within 3 hours, achieving 14% higher accuracy than Palantir’s Metropolis solution. Lab test reports (n=45, p<0.05) show traditional solutions’ false positive rates surge from 7% to 28% when processing over 43 data sources, but adding dynamic Bayesian filtering layers maintains key intelligence extraction speed at 4.7±0.3 seconds/item even under 87TB/day data deluge. This is like instantly finding all cloned vehicles in 100,000-car/minute highway traffic.

Smart Monitoring Early Warning Systems

In March’s dark web leak of 2.1TB data, a border province’s gas station surveillance logs showed 47-second timestamp discrepancies with satellite transit data. Bellingcat detected 12% confidence deviation using open-source tools, triggering secondary alerts at a Xinjiang command center – what might seem technical glitches actually allowed missile launchers disguised as oil tankers to complete position transfers.

At 2 AM last Tuesday, Shenzhen Customs’ container scanning system alerted. X-ray showed normal electromechanical equipment, but thermal imaging revealed 6℃ lower internal temperature – perfect for storing certain missile guidance components. The algorithm incorporated a 2022 CAS Automation Institute patent (CN114638022A) that reduced container interlayer scanning errors from 5cm to 2mm.

• 23 VPN relay attacks automatically intercepted overnight on a government cloud platform, matching T1588 techniques in Mandiant report#2023-04512
• Hohhot power grid misjudged wind power fluctuations as cyberattacks, later traced to Mongolian sandstorms affecting solar panel reflections
• A TikTok MCN’s batch accounts got flagged for concentrated posting during 10-12 AM Moscow time (3-5 PM Beijing time)

Current engineering headaches involve data pollution. Last year’s subway facial database suddenly included 300+ Wandering Earth movie stills from an intern’s unsecured AI art model test. This prompted new regulations: all training data must use Beidou timing chip encryption with under 0.5ms timestamp errors. Shanghai’s smart manhole covers caused laughs – vibration sensors meant for pipeline monitoring interpreted morning delivery bike patterns as “Morse code transmissions”. Veteran officers solved it: a breakfast shop dumped waste water daily at 7:15 AM through a specific cover.

Algorithms Mine Dark Web Data

Recently leaked dark web weapon transaction logs coincided with South China Sea fleet exercise coordinate leaks. Mandiant report#MFD-2023-1881 noted encrypted Telegram documents with 89.2 language model perplexity (ppl) – 30% higher than normal coded conversations. A Chinese cybersecurity lab’s patent (ZL202310558299.5) details key operations: using GANs to forge dark web traffic, disguising real data requests as regular onion routing. This is like placing surveillance cameras where vendors can’t distinguish customers from undercover agents.

During last year’s Bitcoin mixer investigation, technicians found a critical bug: dark web data exceeding 2.1TB caused 17% IP drift at Tor exits. They developed dynamic compensation algorithms using triple GPS positioning principles.

• A C2 server’s IP change trajectory showed 400% traffic surge at UTC 2023-04-12T08:17:03±3s in a Beijing data center
• EXIF metadata revealed “Southeast Asian fishing boat” photos used UTC+8 timezone devices

Deep learning’s Achilles’ heel: data cleaning consumes 40%-60% of processing time. Dark web Russian weapon jargon mixed with Vietnamese gambling terms confuses standard NLP models. Military distributed frameworks reduced 1TB cleaning from 58 to 9 hours. Per MITRE ATT&CK T1591.002: A forum “fishing boat manual” used satellite shadow angles to expose Yongxing Island construction progress. Algorithm detected 5-8% to 23% architectural contour deviation spikes against OpenStreetMap.

Virtual Agents Predict Threats

Myanmar border decryption events spiked geopolitical risks to orange level. China’s upgraded intelligence deploys virtual agents for digital prospecting – 24/7 digital prophets. Mandiant report#MFG-2023-8812 noted a Telegram channel’s ppl spiking to 89 (normal＜75), triggering alerts.

Intelligence veterans know aligning satellite timestamps with ground UTC zones is crucial. Zhuhai lab tests (n=45, p＜0.05) showed building shadow angle deviations＞3° drop camouflage recognition from 91% to 67% – like spotting fake product photos.

• 【Threat Prediction】2AM dark web scans → noon satellite heatmap cross-checks → UTC+8 risk mapping
• 【Data Scrubbing】Delete 24-hour data around Roskomnadzor blocks (noise filtering)
• 【Camouflage Detection】Multispectral analysis keeps vehicle heat signature errors at 7-13%

MITRE ATT&CK v13’s new T1588.003 counters ML model poisoning. Spatiotemporal hashing exposed 15 abnormal heat sources in Myanmar protest videos vs satellite feeds. Palantir’s 20-minute task took domestic algorithms 11 seconds. Notable fail: LSTM models misread nighttime fishing lights as South China Sea military signals. Current algorithms double-check AIS codes and historical trajectories – AI prophets now have bodyguards.

Human-Machine Collaborative Decision Revolution

Last November, 3-second UTC+8 timestamp discrepancies between ground surveillance and satellite data emerged in encrypted channels. When Bellingcat’s matrix showed 12% confidence deviation, a domestic lab used multispectral overlay algorithms to pinpoint disguised warehouses in 14 minutes – showcasing human-AI synergy. Military analysts now view Palantir Metropolis threat models alongside domestic spatiotemporal hashing systems. Both conflicted on border clash predictions (83% vs 67-72%), later traced to Russian dark web data contaminating training samples.

Machines’ literal interpretations cause headaches. AI flagged all ambiguous diplomatic phrases (“closely monitoring”, “grave concern”) during an embassy leak. Veterans taught the system 68 variants of diplomatic jargon using historical archives.

• 【2023-07-14 UTC+8】Border base captured Telegram ppl=89 (normal＜70), triggering Level 3 alert
• 【MITRE ATT&CK T1583.002】Tracked APT group C2 servers switching 19 country nodes in 7 hours
• 【Patent CN202310567891.3】Building shadow verification boosted camouflage detection to 83-91%

Current decision chains resemble ping-pong: AI smashes data, humans delicately respond. Machines scan 3.7TB historical images instantly, but human eyes check Google Earth street views for cloud-obstructed shadows. AI once rejected rusty tankers, unaware of humidity-induced oxidation rates. The ultimate failsafe: self-destruct protocols activating manual mode when ppl＞85 for 15+ minutes or abnormal timezone patterns emerge (e.g. UTC+8 & UTC-5). Like gear-shifting to neutral, requiring instant human takeover.

Operational Case Studies

Bellingcat exposed 42GB dark web leaks using satellite shadow verification – Chinese border checkpoint vehicle heat signatures contradicted Sentinel-2 images with UTC timezone mismatches. A Telegram channel’s ppl spiked to 89, exposing weather data-masked encrypted commands. Standard operating procedure for missing boat AIS signals: 1. Compare 72-hour multispectral satellite data 2. Search nearby base station IMEI fingerprints 3. Cross-verify diesel purchase fluctuations 4. Run Benford’s Law analysis (benford_py v2.1.7) 5. Trigger alerts when three data sources show＞18% confidence variance

An NGO’s “wildlife” photos had GPS altitude/pressure discrepancies. Checking camera Docker fingerprints revealed 14 timezone-location mismatches – like using food delivery addresses for drug busts, exposing Yunnan border intelligence operatives disguised as birdwatchers. Per MITRE ATT&CK T1595.002, systems activate Tor exit collision detection when dark web scraping exceeds 2TB/hour (equivalent to 300 Walmart surveillance feeds). Mandiant report#MFG-2023-1157 noted encrypted channel message intervals spiking from 7 to 83 seconds – like suddenly speaking Morse code. Current game-changer: vehicle thermal analysis using satellite engine heat to verify parking duration vs customs declarations. This exposed signal jammers disguised as freezer parts with 12-37% higher radiation – more obvious than measuring boiling water with thermometers.

CONTACE INFORMATION:

• Aliyun mail: jidong@zhgjaqreport.com
• Blog: https://zhgjaqreport.com
• Gmail：Jidong694643@gmail.com
• Proton mail：Jidong694643@proton.me
• Telegram/Whatsapp/signal/Wechat: +85244250603
• Dark Website: http://freedom4bvptzq3k7gk4vthivrvjpcllyua2opzjlwhqhydcnk7qrpqd.onion