How Do AI Image Detectors Work and Are They Accurate?

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In this post, we'll take a look at:

Generative AI is improving by the minute. By “better,” we mean that everything it generates is more human-like than ever before. 

This poses a significant problem for us humans, as the amount of content we cannot trust has grown exponentially. This is particularly true for images – whether they are online or in print – no one can be certain anymore if the image they are seeing is real.

Creating AI images is easy; just enter a prompt into a tool like DALL-E, Stable Diffusion, or Midjourney, and you will get the desired result in seconds. While some tools perform better than others, there is something for everyone, regardless of their intentions. 

However, some fake images, such as political deepfakes, can cause harm and create unrest among the general public, so it’s valuable to know if there is an AI that can help you combat AI-generated images.

The short answer is yes – say hello to AI image detectors. While the concept isn’t new, these tools are continually improving. They are constantly evolving to keep up with the latest AI image generators, and they are becoming more reliable and accurate as they train on more images.

In this post, we are going to examine how AI image detectors work and assess their accuracy.

How Do AI Image Detectors Work?

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An AI image detector is a tool that uses AI to check whether an image is authentic, AI-generated, or altered from its original form.

Essentially, these tools look for clues in the image and, in some cases, its metadata to reveal its sneaky origins. For this type of detective work, these tools employ various techniques or a combination of these approaches to improve their accuracy:

  • Using AI models: Most tools today utilize their own machine learning models that have been trained to recognize the subtle differences between AI-generated and real images.
  • Pixel patterns: This involves examining the individual pixels that make up the image. AI-generated images often exhibit unusual patterns or irregularities at the pixel level that differ from naturally captured photographs.
  • Image noise: Real photos tend to have a specific type of grain or noise pattern, while AI-generated images may lack this natural noise or display atypical noise patterns that can be detected.
  • Compression: Photos are compressed into file formats like JPG. AI images may be compressed in strange ways that do not match those of real cameras.
  • Metadata clues: Images contain data about when and how they were created. Fake AI images may have missing or inconsistent metadata compared to real ones.
  • Analyzing origins: Some tools look at where an image first appeared online and how it spread to try to trace whether it has a real origin story or just popped up out of nowhere.

Future Developments:

It’s important to note that these techniques are constantly evolving, as AI image generation models also improve and try to evade detection.

As there is public pressure to regulate AI-generated content, such as AI-generated images, one potential enforcement method could be AI watermarking—adding unique marks to AI output by the tools that generate the images in the first place. 

However, this is the best-case scenario and could take years to implement.

How Accurate Are AI Image Detectors?

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The short answer is that if you take your time to learn about what a certain AI image detector can and cannot do, it can be very accurate.

As we have tested many of them, let’s discuss in detail what we have found.

Where do AI image detectors shine?

  • Detecting real photos: Most tools that we have tested can easily detect real human-taken photos, even the less reliable tools seem to get it right. The lowest confidence level on a high-quality real image we have seen is 75%. The key is image quality: higher quality yields better accuracy. Always use the best available image for analysis.
  • Detecting fully AI-generated images: If the image was generated using a tool like DALL-E, Stable Diffusion, or Midjourney, and sent straight to the detector, they can most likely detect it. Even if you manipulate the metadata, most of them will still detect it.

Where do AI image detectors fall short?

A large number of publicly accessible AI image detectors fall short when detecting a real image that has been altered by AI to add extra artifacts. They often categorize such an image as being real, which could be potentially dangerous, as you can easily change the meaning of an image.

If you are interested in which tools do not make this error, you should check out our AI image detectors directory here. Just remember to look at the ratings the tool received in our testing.

To make it even easier for you, some good options that we’ve found for the non-professional user include Fake Image Detector and WasItAI. If you are interested in more detailed results, go with AI or Not. Most of the time, these tools can accurately determine when an image has been altered with AI with high confidence.

Final Thoughts and Conclusion

The plethora of fake images that have accompanied generative AI are a blessing for some and a headache for others. AI-generated images can make cool profile pictures but can also be used in scams and cause public disturbances.

However, for those who want to determine if an image is fake or real, AI image detectors exist. Essentially, these tools use various ever-evolving techniques to look for clues in the image to reveal its sneaky origins.

These tools are relatively accurate if you know how to use them—use the highest quality image possible and make sure that you understand the capabilities of the tool you are using.

If you need to detect real or fully AI-generated images, most tools will suffice. If you need to detect real images that might have been altered with AI, you will need to do more homework and check out our AI image detectors directory with tool ratings here.

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