Advanced Geofencing: Detecting Fake GPS and Mock Locations

Any attendance system built around GPS has to answer an uncomfortable question: what stops someone from just faking their location? Fake-GPS apps are freely available on Android, and a guard determined to avoid actually being on site could, in theory, try to spoof their coordinates rather than skip the punch entirely. Raksha Kavach's advanced geofencing exists specifically to close that gap.

The mock-location signal

Android's location APIs expose a specific flag when a coordinate is coming from a mock-location provider — the mechanism fake-GPS apps use to override the device's real GPS/network location — rather than genuine hardware. This isn't a workaround or a heuristic; it's a signal the operating system itself provides. Raksha Kavach reads this flag on every attendance punch, and a punch reporting a mocked location is rejected outright, regardless of what coordinates it claims.

Why this is a hard rejection, not just a flag

Some systems merely flag a suspicious punch for later review — accepted at the time, questioned afterward. Raksha Kavach takes a stricter position for mock locations specifically: the punch is rejected at the moment it's attempted, with no attendance record created at all. The guard has to correct the situation (disable the fake-GPS app) and punch in for real.

Two geofence shapes for real site layouts

A single circular radius works well for a compact site, but not every site is shaped like a circle. Raksha Kavach supports drawing a polygon boundary that follows a site's actual perimeter — useful for large or irregularly shaped campuses where a radius wide enough to cover the far end would also loosely cover the street outside. Admins choose whichever shape genuinely fits each site from the Sites page.

GPS accuracy as supporting evidence

Beyond mock-location detection, every punch also records the GPS accuracy reported by the device — how confident the location fix itself was. This is informational rather than an automatic block: a supervisor reviewing attendance can spot a pattern of unusually poor accuracy readings for a particular guard or site and investigate further, rather than the system silently accepting or rejecting on accuracy alone.

Device context on the record

Attendance records also carry fields for device model, network type and battery level — additional context that helps a supervisor reviewing a suspicious entry build a fuller picture, beyond just the location and accuracy numbers. This kind of context expands as the mobile app is extended to capture more of it directly from the device.

The honest goal of advanced geofencing isn't "impossible to fool" — no location system built on a phone's self-reported GPS can promise that. It's raising the bar meaningfully and giving supervisors real, specific evidence instead of a bare coordinate.

iOS is a known limitation

Apple's iOS doesn't expose an equivalent OS-level mock-location signal the way Android does, so this specific detection currently works on Android devices. This is a genuine platform limitation worth being upfront about rather than glossing over.

Conclusion

Geofencing without fake-GPS detection is trusting a number the phone hands you. Raksha Kavach adds the checks that make that number harder to fake in the first place, and gives supervisors the context to catch what still slips through on review — a layered approach rather than a single silver-bullet claim.