Images generated by AI technologies are increasingly becoming increasingly rare in the digital environment, where the distinction between them and real photographs is becoming increasingly rare. These images, known to be generated by AI tools such as ChatGPT, Midjourney, and Google Gemini, can be identified through detailed analysis and various technical methods. This identification process is critical for digital literacy.

How are AI-generated images identified?

One of the primary ways to understand AI-generated images is to analyze them at the pixel level. AI-generated images are particularly notable for excessive smoothness and artificial softness in the edge areas.

The natural image noise found in real photographs is generally absent in AI images, making them easily distinguishable. Furthermore, AI images exhibit distinct compression artifacts and JPEG artifacts. These artifacts can be detected using image editing software.

Inconsistencies in human anatomy and physical laws are highlighted when examining visual details. For example, the pupils in AI-generated human faces are often perfectly symmetrical.

In contrast, pupils in real people are rarely perfectly symmetrical. Errors can be observed in anatomical details such as the number of fingers, tooth arrangement, and hairline. Shadow directions, consistency of light sources, and perspective rules are also among the factors that need to be checked.

Online detection tools are also used to distinguish AI images. Platforms like Decopy AI analyze images with machine learning algorithms, suggesting the possibility of AI-generated images.

Using these tools multiple times ensures accuracy in detection. Furthermore, reverse image search engines like Google Images or TinEye allow users to examine the online history of images. Because AI-generated images are generally newly created, they lack long-term historical records.

Metadata analysis is also an important method. The EXIF data of real photographs includes the camera model, shooting date, and location information. In AI images, this data is either missing or contains fake information. When examining file properties, images created in square formats (such as 512×512, 1024×1024) are highly likely to be AI-generated. File creation and modification dates also provide important clues.

In terms of visual consistency, discrepancies between background and foreground objects can be observed. Perspective errors, inconsistent aspect ratios, or physically impossible situations indicate AI-generated content. Furthermore, repetitive and abnormally dense patterns in textures, patterns, or background elements indicate AI-generated content.

Due to the rapid advancement of AI technologies, it is noted that these current detection methods may become inadequate in the near future. However, for now, these methods offer valid and effective ways to identify AI-generated images. Knowing these detection methods is crucial for accessing accurate information in the digital world and protecting against misleading content.