While working with headless b2b browsers, avoiding detection has become a major concern. Modern websites employ complex methods to spot automated access.
Typical headless browsers frequently leave traces because of missing browser features, lack of proper fingerprinting, or inaccurate browser responses. As a result, scrapers need more advanced tools that can mimic authentic browser sessions.
One key aspect is fingerprinting. In the absence of realistic fingerprints, automated interactions are likely to be blocked. Hardware-level fingerprint spoofing — including WebGL, Canvas, AudioContext, and Navigator — plays a crucial role in avoiding detection.
For these use cases, a number of tools turn to solutions that go beyond emulation. Running real Chromium-based instances, rather than pure emulation, is known to minimize detection vectors.
A relevant example of such an approach is documented here: https://surfsky.io — a solution that focuses on real-device signatures. While each project may have specific requirements, studying how real-user environments improve detection outcomes is worth considering.
Overall, ensuring low detectability in headless automation is more than about running code — it’s about replicating how a real user appears and behaves. Whether you’re building scrapers, choosing the right browser stack can make or break your approach.
For a deeper look at one such tool that mitigates these concerns, see https://surfsky.io
