How to Tell if Someone Else’s Apple AirTag Is Tracking You


The biggest benefit of Apple’s AirTags is that they help you find your belongings, whether you’re looking for lost keys or keeping track of your luggage while traveling. But AirTags can also be used to track you without your knowledge. 

AirTags work by combining built-in sensors, wireless signals and Apple’s wide Find My network to let you keep tabs on your valuables. If you ever lose your wallet with an AirTag inside, for example, you can use the Find My app to locate it on a map, have it play a sound to help you find it nearby, or mark it as “lost,” which allows other Find My users to help you find it. 

One of the biggest complaints about AirTags, however, is that someone with malicious intent could easily slip one of the tiny tags into your bag and then track your movements without your consent. Multiple people have reported AirTag-related stalking incidents where the victims didn’t know the trackers were placed on them until much later.  

Apple and Google (Android users have their own choice of Bluetooth trackers, such as the Moto Tag, which works with Google’s Find Hub) have since collaborated on an industry standard that alerts the user if a device is being used to track them without their knowledge. Thanks to this collaboration, Android users will be able to know if an AirTag is being used to track them, too. 

Apple, for its part, has also made some changes in the past few years that improve the ability to detect an unwanted AirTag. In the initial rollout, an AirTag would make a sound three days after it’s separated from its paired device. Now, that duration is 8 to 24 hours. If you have unwanted tracking notifications enabled (which we’ll get to below), you’ll receive an audible alert.

We should note here that the new AirTag is 50% louder than the first-generation model, and would therefore be theoretically better at alerting you to the unwanted AirTag. Apple has also said that the speaker on the second-gen AirTag is harder to remove than on the first-gen model, in case bad actors try to remove it. 

an iPhone with Find My on the screen next to an AirTag 2

Apple’s Find My helps you set up and track an AirTag. It can also help notify you if an unwanted tracker is detected.

Patrick Holland/CNET

Detecting unwanted trackers

To be able to detect unwanted trackers, first enable unwanted-tracking notifications. For AirTags or other Find My accessories, these pop-up notifications (e.g., “AirTag found moving with you”) are available on devices with iOS 14.5 or later. For other Bluetooth tracking devices, these notifications are enabled on iOS 17.5 or later. 

You should enable Location Services, Find My iPhone, Bluetooth and Allow Notifications. Here’s how:

  • Head to Settings, then Privacy & Security, then Location Services and toggle it on. 
  • After that, head to Settings, then Apple Account, select Find My and turn Find My iPhone on. 
  • To enable Bluetooth, go to Settings, then Bluetooth and turn that on. 
  • Then go to Settings, then Notifications, scroll down to Tracking Notifications and toggle on Allow Notifications. Make sure airplane mode is off, or you won’t receive tracking notifications. 

Watch this: Testing the New AirTag, While Tim Cook’s White House Visit Sparks Apple Boycott Calls

What to do when you get the tracking notification

If you do get a notification like “Unknown tracker alert” or “Item detected near you,” you can try to find the unwanted AirTag by tapping it. Tap continue and then tap Play Sound or tap Find Nearby to locate the AirTag in question. 

If it doesn’t play a sound or you’re unable to find it, the item may no longer be on your person. Apple suggests checking your other belongings or the area around you, just in case. If you want to review the notification at a later time, you can open the Find My app, tap Items and then tap Items Detected With You.

Be aware that there are often “false positives,” when notifications are triggered when someone nearby has a tracker on them. If you’re traveling on a train, plane or bus, waiting in line or seated in a public space, a mistaken tracking alert could stem from glitches or high-density Bluetooth environments. 

If you get an alert, though, it’s always a good idea to take it seriously and investigate what might be causing it.

If you do find an AirTag that doesn’t belong to you, hold the top of your iPhone near the tracker until you see a notification. Tap it, and this will launch a website that provides information like its serial number, the last four digits of the phone number or a blurred-out email address of its owner. If the AirTag is marked as “lost,” you may see a message with instructions on how to contact them. 

If you’re concerned that the tracker is being used to monitor your movements and location, Apple advises taking a screenshot of the information above for your records. You can then disable the AirTag by pressing down on the back of the AirTag, turning it counterclockwise to remove the cover and removing the battery.  

Of course, before making any of these changes, it’s important to come up with a safety plan, especially if you’re afraid you’re being tracked by a current or former abusive partner. Contact your local law enforcement if you feel like your safety is at risk, or the National Domestic Violence Hotline 800-799-SAFE (7233).





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What Are Selection Techniques

Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. The techniques have the ability to choose the relevant variables according to the type of user’s problem. In case any data comes up that is not relevant to the requirement, it tends to slow down the efficiency process of the model and also decrease the accuracy. Therefore, it is very important to have appropriate feature selection techniques for the models in order to have better outcomes and accuracy. 

The main idea of working with selection techniques is to manually extract the relevant settings from the parent set to have high-accuracy model structures.

Feature Selection in Machine learning

The techniques are divided into the category of supervised and unsupervised learning. These two categories are further divided into 4 main methods for selecting the features.

Filter Method :

There are statistical ways for selecting the features using the filter method. The features are selected in the pre-processing stage as there is no learning process involved in this. The aim of this approach is to filter out the unrequired and irrelevant features by using matrices and ranking methods. The most important advantage of using the filter method is that it does not overfit the data.

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Wrapper Method :

In this method, a user makes different combinations that are evaluated or compared with a lot of other possible combinations. In this way, the feature selection is done. A subset of features is selected and the algorithm is trained based on the subset. The output of the algorithm then decides if the features will be added or not. This method is further based on 4 types which are:

  • Forward Selection : This process takes in an empty feature set. It keeps adding a feature to each interaction and checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Backward Elimination : This approach is the complete opposite of the forward selection approach. The process takes in all the features of the algorithm and then keeps removing a feature one by one on each iteration. It checks the progress simultaneously as if it is improving or not. This method keeps on iterating unless there comes a feature that does not improve the progress of the model.
  • Exhaustive Feature Selection : It is the most common approach for feature selection as each feature is set as brute-force. The approach aims to try various combinations of features in order to give the best outcome.
  • Recursive Feature Elimination : This method is based on the greedy approach as its features are selected in a smaller amount. An estimator is made to test every set of features designed and thus we get an outcome of the best features.
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Embedded Method :

This is a great method for feature selection as it has the advantages for both filter and wrapper methods collectively. The processing time in the embedded method is very high just like the filter method, however, they provide more accurate outcomes.

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There are a few techniques involved with embedded methods which are:

  • Regularisation : This aims at regularising the feature selection method simply by adding a penalty if the data gets overfitted in the model. The points shrink to a value of 0 and they are eliminated from the dataset. The types of regularizations are L1, L2, L3, etc. 
  • Random Forest Importance : This technique involves a lot of tree-based approaches to select the features for an algorithm. A number of decision trees are involved in this as the ranking of nodes is performed in all the trees to get the results. After filtering out the irrelevant nodes, a subset of the most relevant nodes creates a final selection of features.
Hybrid Method :

This approach takes in features as small-sized samples. The main idea is to select the features using instance learning. The features that correspond to the instances are selected as they are relevant to the algorithm.

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Feature Selection Models

Supervised Model :

This model is defined as the class of machine learning methodologies where the user can train with the help of continuous and well-labelled data. For instance, the data can be historical data where the user wishes to predict whether a customer will take a loan or not. Supervised algorithms tend to train over the well-structured data after the preprocessing and feature characterization of this labelled data. It is further tested on a completely new data point for the prediction of a loan defaulter. The most popular supervised learning algorithms are the k-nearest neighbour algorithm, linear regression algorithm, logistic regression, decision tree, etc.

This is further divided into 2 categories:

  • Regression: The dealing of output variables is done using regressions as it includes graphs, images, etc. For example to determine age, height, etc. 
  • Classification: it helps in classifying different objects such as yellow, orange, wrong or right, etc.
Unsupervised Model

This model is defined as a class of machine learning methodologies where the tasks are performed using the unlabelled data. Clustering is the most popular use case for unsupervised algorithms. It is defined as the process of grouping similar data points together without manual intervention. The most popular unsupervised learning algorithms are k-means, k-medoids, etc. 

This is further divided into 2 categories:

  • Clustering :This means when the machine requires an inherent group while training the data.
  • Association :This category has a set of rules which helps in the identification of massive data. For example, a list of students who could be interested in artificial intelligence as well as machine learning.
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How To Choose a Feature Selection Model

It is very important for machine learning engineers as well as researchers to understand which feature selection model is most suitable for them. The most data types are known by the engineer, the easier it will be for him to choose properly and wisely. This whole concept is based on 4 main approaches which are:

  • Numerical Input, Numerical Output : There are two methods used in this technique which are Pearson’s correlation coefficient and Spearman’s Rank Coefficient.  The numerals are basically used for the prediction of regression models for continuous numerical such as int, float, etc. 
  • Numerical Input, Categorical Output : There are two methods used in this technique which are the ANOVA correlation coefficient, and Kendall’s rank coefficient. The numerals are basically used for the classification of predictive models for continuous numerical such as int, float, etc. 
  • Categorical Input, Numerical Output : This is a case of the prediction of regression models using input based on categories. The process is the same as numerical input, and categorical output but in a reverse fashion. 
  • Categorical Input, Categorical Output : This is a case of classification of predictive models using both categorical inputs as well as outputs. The main approach affiliated with this method is the Chi-squared method. Moreover, information gain can also be used with this technique.

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Conclusion:

The process of selecting features in machine learning is a vast concept and it involves a lot of research to select the best features. However there is no hard and fast rule for making the selection, it all depends on the type of model and its algorithm and how a machine learning engineer wants to pursue it. Selection techniques in machine learning help in reducing the noise by taking in only the relevant data after the pre-processing. 

In this article, we have talked about various feature selection methods that use certain algorithms for making the best possible outcomes and why we should make this feature selection method. Along with this, we have talked about how we can finalise the best feature selection model to work with.

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