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Camera Detector App

Camera Detector App

HMA Mobile LLC

Productivity免费v1.1.12
App Store
评分

4.4

9,816 条评分

星级

★★★★☆

最近更新

2025年10月1日

发布日期

2023年6月18日

更新内容

v1.1.12

*** Upgraded AI-Powered Detection *** - Military-grade technology scan for WiFi, bluetooth & listening devices and GPS trackers with precision. - Fix bugs

应用信息

开发者
HMA Mobile LLC
分类
Productivity
价格
免费
版本
1.1.12
App ID
6449518978

简介

Our app helps you scan your surroundings and Wi-Fi networks to check for unusual devices that might affect your privacy, especially in important places such as hotels, bathroom, your home. Stay safe and private. Find the new Wi-Fi, Network, Bluetooth & AI Lens Scanner, GPS trackers & more. Features: - AI Camera & Lens Detection - WiFi Scanner - Bluetooth Scanner - Scan History This app is designed as a supporting tool to help you scan your Wi-Fi, Bluetooth Network nearby and identify unfamiliar devices through iPhone Camera with applied AI training of object detection. It does not guarantee 100% detection of hidden cameras. You can choose a weekly subscription or yearly subscription (3 days trial). – Payment will be charged to iTunes Account at confirmation of purchase (After free trial period if offered). – Subscription automatically renews unless auto-renew is turned off at least 24-hours before the end of the current period. – Account will be charged for renewal within 24-hours prior to the end of the current period, and identify the cost of the renewal. – Subscriptions may be managed by the user and auto-renewal may be turned off by going to the user’s Account Settings after purchase. – Any unused portion of a free trial period, if offered, will be forfeited when the user purchases a subscription to that publication, where applicable. Privacy Policy: https://sites.google.com/view/hidden-camera-privacy-policy/ Terms of Use: https://sites.google.com/view/hidden-camera-terms-of-use/

下载量预测

专业 · 预览

预估总下载量

982K654K2.0M
保守估计乐观估计

19K

低 / 月

28K

预估 / 月

56K

高 / 月

基于9,816 条评分
假设评分率1.0%
应用年龄35 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。