While that sounds obvious enough, unless you know the original creator and have their permission to use it, you could be exposing yourself to potential copyright claims in the future. Even using "copyright-free" images from sites like Flickr isn't always foolproof, as creators can claim other people's work as their own.

So, how do you navigate this minefield safely? One option is using images in the public domain, but you are often stuck with low-quality or irrelevant images. But there is another way, something called reverse image searches.

But what is it? And how do you do that? Let's find out.

As the name suggests, a reverse image search is a technique for querying the internet for an image to see if any other exact examples are present. More technically, a reverse image search is a content-based image retrieval (CBIR) query method that gives the CBIR system a sample image it will use to base its search.

In particular, reverse image search is defined by the fact that it doesn't have any search terms. This means that a user doesn't have to guess at keywords or terms that might or might not bring up the right results.

Delmaine Donson/iStock 

A reverse image search lets users find content related to a specific sample image, find out how popular an image is, and find altered versions of the image and works based on it.

There are a few products out there you can use to perform this sort of search, but Google’s reverse image function is one of the most popular.

More accurately called 'Google Images' or 'Google Search by Images', the function had existed since 2011 when the internet giant began to offer it. This function is most useful for specific users for various reasons.

For example, you can use it to check for the original owner of an image to verify or check for any potential copyright issues using it. It can also help confirm or fact-check a particular image used in a news report, meme, etc.

Great, so how do you do it?

There are a few ways to use it, as it happens.

But, in any case, you’ll first need to navigate to Google Images and select the camera symbol from the search results. From there, you can post a link to an online image or upload an image from your computer.

For the latter, you need to right-click the image and choose “Copy Image Address” to paste a link to an internet image. The search box can then be used to paste this into, and the default setting for this should be Paste Image URL.

For the former, you can drag a file from your computer into the search bar using Chrome or another browser like Firefox.

If you use Google’s Chrome, however, you get the added benefit of running a reverse image search anytime you encounter an image on a website. To do this, right-click the image and choose Search Google for Image.

The search results generated do vary from the typical Google searches you are used to, but they are easy to understand.

metamorworks/iStock 

Typically, when looking for photographs on Google Images, you type in a term like "flowers" or "landscape," and Google tries to show you the images that are most closely connected to that term.

But when you use reverse image search, you first upload an image or type in the URL of an image on the web. Giving Google a clear idea of what you're looking for lets it finds images and information that are particularly relevant to that image.

Great, but how does this seemingly magical tool work?

Well, it's not magic, as you've probably guessed. Reverse image searching, irrespective of which service you use, each search engine's reverse image search works a little differently, and they don't share their exact algorithms.

Still, the basic ideas are out there and easy to understand, and tend to work on a few basic concepts. These are as follows.

The first is something called fingerprinting.

As the term suggests, each image has specific characteristics similar to the fact that no two fingerprints are alike (usually).

Pictures may be more unique than fingerprints because it is almost impossible for two images to have the same arrangement of pixels. In contrast, the chance of two fingerprints having the same structure is around 1 in 64 billion, which is practically impossible to duplicate naturally.

But how, you might wonder, how can a picture be fingerprinted? Well, that depends entirely on the algorithm.

The steps used to determine an image's unique arrangement of pixels tend to be different, but most follow the same basic pattern.

First, each algorithm needs to measure the image's features, including color, texture, gradients, shapes, relationships between different picture parts, and elements like Fourier transforms.

Fourier transforms, if you are unfamiliar with the term, is a mathematical operation that breaks down functions into their frequency components. We won't go deeper into this concept as it is a little off-topic. Still, this sophisticated process generates a characteristic pattern that can be used to distinguish data sets.