How to Bypass ML Protection on Facebook Ads, Google, and TikTok: Methods to Combat Anti-Bot Systems in 2025
Did you get banned from your Facebook ad account the day after launching? Were 10 Instagram accounts blocked simultaneously? Did your scraper stop collecting data from the marketplace? The reason is simple — modern platforms use machine learning (ML) to automatically detect suspicious activity. In this article, we will explore how ML anti-bot systems work and which methods can effectively bypass protection in 2025.
This article is for arbitrageurs, SMM specialists, marketplace sellers, and anyone working with multiple accounts or automation. No theory — just practical methods with setup examples in popular tools.
How ML Anti-Bot Systems Work: What Facebook and Google See
Modern anti-bot systems are not just simple rules like "block if 100 requests per minute." They are neural networks trained on millions of examples of real and bot behavior. Facebook, Google, TikTok, Wildberries — all major platforms use machine learning for protection.
The principle of ML protection: the system collects hundreds of parameters about each user and calculates a "suspiciousness score" from 0 to 100. If the score exceeds a threshold — a block or additional verification (captcha, phone verification, account freeze) is triggered.
Practical Example: An arbitrageur created 20 Facebook Ads accounts through Dolphin Anty with different proxies. After 3 days, all accounts were banned in a wave. The reason — the ML system detected the same pattern of actions: all accounts were created at the same time, used the same sequence of ad settings, logged in at the same hours. Different IPs did not help — the behavioral pattern was identical.
What Data ML Systems Collect
ML models analyze three main categories of data:
| Data Category | What is Tracked | How ML is Used |
|---|---|---|
| Technical Parameters | IP address, User-Agent, screen resolution, Canvas fingerprint, WebGL, fonts, timezone, browser language, plugins | Creating a unique digital fingerprint of the device to link accounts |
| Behavioral Patterns | Typing speed, mouse movements, pauses between actions, session times, login frequency, click order | Identifying automation and bots based on non-human patterns |
| Contextual Signals | Geolocation vs interface language, IP and timezone matching, device change history, connections between accounts | Detecting discrepancies and anomalies in the user profile |
The key difference between ML systems and regular rules: they learn. If you found a bypass method that worked a month ago — it doesn't mean it works now. ML models are constantly retrained on new data about bots and fraudsters.
7 Signals That Give Away a Bot or Multi-Account
ML systems do not look for one "smoking gun" — they calculate the probability based on a combination of signals. Here are the strongest indicators that increase the suspiciousness score:
1. Identical Digital Fingerprint
What it is: A combination of browser parameters that creates a unique device ID. Canvas fingerprint, WebGL hash, font list, audio context, screen parameters.
Why it's critical: If two accounts have identical fingerprints — it’s 100% one person on different accounts. The ML model instantly links the profiles.
Ban example: An SMM specialist managed 15 Instagram accounts for clients through regular Chrome with different proxies. The fingerprint was identical — all accounts were banned for multi-accounting.
2. Mismatch Between IP Address and Other Parameters
What it is: The IP shows Moscow, but the timezone is set to New York. Or a mobile proxy from Russia, but the browser language is English.
Why it's critical: ML models are trained on millions of real users and know typical combinations. Discrepancies are a strong signal of forgery.
Common mistake: Bought residential proxies from the USA for Facebook Ads, but forgot to change the timezone and language in the anti-detect browser. The account got banned on the first login.
3. Robotic Behavior Patterns
What it is: Identical intervals between actions, perfectly straight mouse movements, instantaneous clicks, no typos when entering text.
Why it's critical: A human cannot perform actions with millisecond precision. ML models easily distinguish automation from manual work.
Real case: A Wildberries scraper made requests exactly every 5 seconds. After an hour, the IP got banned. After adding random delays of 3 to 8 seconds — the scraper worked for months.
4. Suspicious Frequency of IP Changes
What it is: An account logs in from different IPs every hour or conversely — never changes IP for months.
Why it's critical: Real users have predictable patterns: home WiFi in the evening, mobile internet during the day, office IP during working hours. Anomalies raise suspicion.
Beginner's mistake: Using rotating proxies for Facebook Ads accounts. The IP changes every 10 minutes — the system sees that the "user" teleports between cities. Ban is guaranteed.
5. Mass Identical Actions in a Short Period
What it is: Creating 10 accounts in an hour, launching 20 ad campaigns simultaneously, mass messaging.
Why it's critical: ML models analyze not only individual behavior but also patterns at the platform level. Synchronous actions are a clear sign of automation.
Arbitrage case: A team launched 50 TikTok Ads accounts in one day through different proxies and browsers. All used identical creatives and targeting settings. A week later, a wave ban — the ML system linked the accounts by behavioral pattern.
6. Using Data Center Proxies on Platforms with Strict Verification
What it is: IP addresses from data centers (AWS, Google Cloud, Hetzner) instead of real internet providers.
Why it's critical: ML systems have databases of all known data centers. Facebook, Google, Instagram almost instantly detect such IPs as suspicious.
Where to use: Data center proxies work for scraping some sites and marketplaces. But for social networks and advertising platforms, only residential or mobile proxies are needed.
7. Lack of "Warming Up" a New Account
What it is: A new account immediately starts aggressive activity: launching ads with a large budget, mass following, sending hundreds of messages.
Why it's critical: ML models are trained on millions of real users and know how newcomers behave. Aggressive activity from day one is a clear sign of a bot or a purchased account.
Correct approach: For the first 7-14 days, imitate a regular user: browse the feed, like, comment, fill out the profile. Gradually increase activity.
Fingerprint Spoofing: Setting Up Anti-Detect Browsers
A digital fingerprint is the first thing ML systems check. If the fingerprints of two accounts match by 90% or more — they will be linked regardless of using different proxies.
Anti-detect browsers solve this problem by creating a unique fingerprint for each profile. Popular solutions in 2025:
| Browser | For Whom | Features | Price From |
|---|---|---|---|
| Dolphin Anty | Arbitrage, SMM | Simple interface, templates for Facebook/Google, action automation | $89/month (10 profiles) |
| AdsPower | E-commerce, mass work | Multiple profiles, API for automation, action synchronization | $5.4/month (10 profiles) |
| Multilogin | Professionals | Maximum fingerprint protection, two engines (Chromium + Firefox) | €99/month (10 profiles) |
| GoLogin | Beginners, budget | Low price, cloud profiles, easy setup | $24/month (100 profiles) |
| Octo Browser | Teams, agencies | Collaboration, access rights, detailed logs | €29/month (10 profiles) |
What Anti-Detect Browser Changes
A quality anti-detect browser changes dozens of parameters to create a unique fingerprint:
- Canvas fingerprint — unique hash of graphic rendering in the browser
- WebGL fingerprint — fingerprint of the graphics card and its drivers
- Audio context — parameters of the device's audio system
- User-Agent — identification string of the browser and OS
- Screen resolution — screen resolution and color depth
- Fonts — list of installed fonts in the system
- Timezone and Geolocation — timezone and GPS coordinates
- WebRTC — blocking real IP leaks through WebRTC
- Languages — browser and system languages
- Plugins — list of installed plugins
- Hardware concurrency — number of CPU cores
- Device memory — amount of device RAM
Critically Important: All parameters must be consistent with each other. You cannot set a User-Agent from an iPhone but have a screen resolution from a laptop. ML systems check the consistency of parameters, and discrepancies increase the suspiciousness score. Use ready-made device templates in anti-detect browsers — they contain realistic combinations of parameters.
Mistakes When Setting Up Fingerprint
Even with an anti-detect browser, you can get banned if you set the parameters incorrectly:
❌ Using the Same Fingerprint for Different Accounts
Some beginners copy profile settings in the anti-detect browser. The result — identical fingerprints and linking of accounts.
✅ Correct:
Create a new profile from scratch or use the "random fingerprint" feature in the browser. Each account should have a unique set of parameters.
❌ Exotic Parameter Combinations
Setting a rare OS (like Linux) with a mobile User-Agent or using outdated browser versions. ML models are trained on real users and know which combinations occur in nature.
✅ Correct:
Use popular combinations: Windows 10 + Chrome, MacOS + Safari, Android + Chrome Mobile. Browser versions should be current or at most 1-2 releases behind.
❌ Mismatch Between Fingerprint and Proxy
Mobile proxy from Russia + desktop User-Agent + timezone from the USA. Such mismatches are a red flag for ML systems.
✅ Correct:
All parameters must match the geolocation of the proxy: IP from Moscow → timezone Europe/Moscow → language ru-RU → geolocation 55.75,37.61 (coordinates of Moscow).
Imitating Human Behavior: Timing and Activity Patterns
Even a perfect fingerprint won't save you if your behavior gives away a bot. ML systems analyze hundreds of micro-patterns: how the mouse moves, how fast text is typed, how much time passes between clicks, and what hours the user is active.
Modern ML models are trained on billions of real sessions and can accurately distinguish a human from a script even by microsecond delays.
Behavioral Signals That ML Systems Track
| Behavior Parameter | What the Bot Sees | What a Human Sees |
|---|---|---|
| Mouse Movements | Straight lines, instantaneous movements, lack of random movements | Smooth curves, micro-oscillations, misses on buttons, trajectory correction |
| Typing Speed | Identical intervals between characters (50ms), no typos, instant text insertion | Variable speed (100-300ms), typos with corrections, pauses between words |
| Action Timings | Exactly 5 seconds between clicks, actions at the same time every day | Random intervals of 3-15 seconds, different activity times, pauses for reading |
| Scrolling Pattern | Scrolling fixed distances, no stops, constant speed | Uneven scrolling, stops on interesting content, going back |
| Session Time | Exactly 10 minutes every time, no pauses, instant exit | Variable duration (5-60 minutes), distractions, tab remains open |
Practical Methods for Imitating Human Behavior
1. Random Delays Between Actions
If you are using automation (scraping scripts, auto-posting on social media), be sure to add random delays. Do not use fixed intervals.
Bad: Delay exactly 5 seconds between requests
time.sleep(5) # Same delay — detected as a bot
Good: Random delay from 3 to 8 seconds
import random
time.sleep(random.uniform(3, 8)) # Imitating a human
2. Imitating Content Reading
When you open a page — do not click the button immediately. ML systems track the time until the first action. A real person reads the text, looks at pictures, scrolls the page.
- After the page loads — pause for 2-5 seconds (imitating reading the title)
- Randomly scroll down 100-500 pixels (viewing content)
- Pause for 3-7 seconds (reading text)
- Move the mouse to the button (no teleporting the cursor)
- Click with a slight miss and correction
3. Natural Activity Patterns
Real people do not work 24/7 with the same intensity. Create realistic activity schedules:
- Login Time: Do not log into the account exactly at 09:00 every day. Vary the time: 08:45, 09:15, 09:30
- Session Duration: Different each time: 10 minutes, 45 minutes, 5 minutes, 1 hour
- Days of the Week: Less activity on weekends (if it's a work account) or more (if personal)
- Breaks: Take pauses in activity. Do not post on Instagram every 2 hours like a robot — make 3 posts in the morning, then a break until evening
Tip for Arbitrageurs: If you are farming Facebook Ads accounts — imitate real user behavior for the first 7-14 days. Log into the account, scroll through the news feed for 5-10 minutes, like 2-3 posts, watch videos. Only after warming up should you start creating ad campaigns. Accounts that immediately launch ads get banned 10 times more often.
4. Using Behavior Imitation Tools
Some anti-detect browsers have built-in features for imitating human behavior:
- Dolphin Anty: The "Scripts" feature allows you to record a sequence of actions with natural delays and then play it back with variations
- AdsPower: RPA (Robotic Process Automation) with settings for random delays and mouse movements
- Browser automation tools: Puppeteer Extra with the puppeteer-extra-plugin-stealth adds human-like behavior to automation
Proper Proxy Rotation: Why One IP Per Account Doesn't Work
A common misconception: "each account needs its own static IP that never changes." This is false. Real users constantly change IP addresses: home WiFi, mobile internet, cafes, offices, travel.
ML systems know this, and both too frequent IP changes (every 10 minutes) and complete lack of changes (one IP for months) are suspicious.
Types of Proxy Rotation and When to Use Them
| Rotation Type | How It Works | When to Use | Risks |
|---|---|---|---|
| Static IP | One IP is permanently assigned to the account | Advertising accounts (Facebook Ads, Google Ads), important social media accounts | If the IP gets banned — the account will too. Unnatural for mobile users |
| Session Rotation | IP changes with each new connection (close the browser — new IP) | Data scraping, mass registration, one-time tasks | Too frequent changes — a red flag for social media ML systems |
| Time-Based Rotation | IP changes every X minutes (5, 10, 30 minutes) | Scraping with protection against rate limiting, price monitoring | NOT suitable for social media accounts — looks like a VPN |
| Pool of 2-5 IPs | The account uses several IPs that alternate naturally | Imitating a real user (home + work + mobile) | Requires switching logic — cannot change IP randomly |
Rotation Strategy for Different Tasks
For Facebook Ads / Google Ads (Arbitrage):
Use static residential proxies. One account = one IP that does not change. Advertising platforms are very sensitive to IP changes and can block an account even with a single address change during an active campaign.
Exception: If imitating a mobile user — you can use mobile proxies with rotation every 10-30 minutes (this is how real mobile internet works). But then all fingerprint parameters must be from a mobile device.
For Instagram / TikTok / Social Media (SMM):
The optimal strategy is a pool of 2-3 IP addresses that alternate in the "home-work-mobile" logic:
- Main IP (residential) — 70% of activity, used in the evening and on weekends
- Work IP (residential from another subnet) — 20% of activity, used during working hours 9-18
- Mobile IP — 10% of activity, random logins throughout the day
This pattern looks natural and does not raise suspicions with ML systems.
For Scraping Wildberries / Ozon / Marketplaces:
Use rotating residential proxies with automatic IP change every 5-15 minutes. Marketplaces track the number of requests from one IP and block it when the limit is exceeded.
Important: Do not use data center proxies — Wildberries, Ozon, Avito have databases of data center IPs and block them automatically. Only residential or mobile proxies.
For Mass Account Registration:
Each registration — new IP. Use rotating proxies with a large pool of addresses. After registration, assign a static IP to the account for further work. Never register multiple accounts from one IP — this results in an instant ban for all accounts.
Critical Mistakes When Rotating Proxies
1. Using One IP for Multiple Accounts Simultaneously
Even if you have perfect fingerprints — if two accounts log in from one IP at the same time, the ML system will link them. One account = one IP at a specific moment in time.
2. Changing Country/City of IP During an Active Session
Logged into an account with an IP from Moscow, and 10 minutes later the IP changed to Vladivostok — this is teleportation over 7000 km. Instant ban. If you are using rotation — IPs must be from the same city/region.
3. Switching Between Mobile and Desktop IPs Without Changing Fingerprint
If you are using a mobile proxy — the fingerprint must be from a mobile device (iOS or Android). You cannot use a mobile IP with a desktop User-Agent — this mismatch is detected by ML systems.
Device Consistency: Linking Proxy + Fingerprint + User-Agent
ML systems check not only individual parameters but also their consistency. This is called "device consistency" — all characteristics must match each other and create a unified picture of a real device.
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