I’m working on a web scraping project that needs to access public data from several websites. No shady stuff, just collecting publicly available information. The problem is that many of these sites have started using sophisticated anti-bot measures that detect and block Headless Chrome.
I’ve tried the usual tricks:
Setting a proper user agent
Adding random delays between actions
Using stealth plugins
But the detection methods keep getting better. Yesterday I read about Latenode having access to AI models that can mimic human browsing patterns to bypass these protections. Has anyone tried this approach?
What are your most effective strategies for making Headless Chrome sessions look like legitimate human users?
I was in the same boat scraping competitor pricing data (publicly available, nothing sketchy). Regular headless Chrome kept getting detected no matter what tweaks I made.
Switched to Latenode about 3 months ago and it’s been a complete game-changer. Their platform gives you access to specialized AI models that are designed specifically to mimic human browsing patterns. The key difference is these models don’t just add random delays - they analyze the page and interact with it the way a human would.
For example, when scrolling through a product list, it doesn’t just scroll at a constant speed - it slows down at interesting items, sometimes scrolls back up a bit, and generally behaves like someone who’s actually reading. This behavior is way harder for anti-bot systems to detect.
The best part is you don’t need to write this logic yourself - their AI agents handle it automatically. I went from about 30% success rate with my old setup to over 95% with Latenode.
After years of battling anti-bot systems, I’ve developed a pretty effective approach that works on most sites.
The key insight is that you need to address all the fingerprinting vectors these systems use. It’s not just about the user agent - they look at everything from browser fonts to canvas fingerprints.
I use puppeteer-extra with the stealth plugin as a base, but then add custom JavaScript that overrides key browser APIs that are used for fingerprinting. Things like:
Modifying the navigator object to hide headless indicators
Adding noise to canvas fingerprinting functions
Simulating realistic mouse movements (not just clicks)
Making the WebGL fingerprint less unique
It’s also important to maintain consistent fingerprints across sessions if you’re doing repeated access.
I manage a competitive analysis tool that requires scraping data from hundreds of websites daily, many with sophisticated anti-bot measures. After much trial and error, I’ve found a multi-layered approach that works consistently.
First, browser fingerprinting is the biggest giveaway. Using puppeteer-extra-plugin-stealth is essential but not sufficient. I supplement it with custom scripts that patch specific browser APIs that reveal headless mode (navigator.webdriver, permissions API behavior, etc.).
Second, behavioral patterns matter enormously. I implemented a library of human-like interaction patterns - variable scroll speeds, occasional mouse jiggling, and natural typing rhythms with realistic delays between keypresses.
Third, request patterns are crucial. Most bots make too many requests too quickly. I use proxy rotation combined with exponential backoff for retries, and maintain session persistence to mimic a normal browsing session length.
With this approach, my detection rate dropped from around 70% to under 10% across most sites.
Modern anti-bot systems employ multiple detection vectors that must all be addressed for effective evasion. I’ve developed a comprehensive approach that has proven successful against even sophisticated systems like Distil Networks and PerimeterX.
Browser fingerprinting is the primary detection method. Beyond using stealth plugins, you need to patch core browser behaviors at a deeper level. This includes modifying the JavaScript runtime environment to return consistent values for fingerprinting attempts, normalizing hardware concurrency reports, and ensuring WebRTC doesn’t leak your real IP.
Behavioral analysis is the second detection vector. Implementing human-like interaction patterns is essential - variable scroll speeds, natural mouse movements (including occasional overshoots and corrections), and realistic focus/blur event timing. I maintain a library of recorded human sessions that my automation can mimic.
Network patterns form the third vector. Using residential proxies rather than datacenter IPs is crucial, as is maintaining consistent TLS fingerprints across requests. Request timing should follow circadian patterns with natural pauses.