To the survey: https://docs.google.com/forms/d/e/1FAIpQLSfujaTdkiyrwagGO4Cv_JjGQ_IIWt9v4tO54aVBSiF7J6a_pw/viewform?usp=header
After finishing the questionnaire, I thought the hardest part was behind me. I was wrong. The next step, choosing the images for the survey, turned out to be just as challenging.
At first, I simply started collecting images that I thought would fit my study. But after a while, I realised that this approach would not work. Every image I included could influence the results, so I needed to think much more carefully about my selection.
*Klick here* (!!Only open this if you have already completed the survey or do not intend to take it!!)
My survey is not only about telling real and fake images apart. I want to know whether people can distinguish between authentic photographs, AI-edited photographs and fully AI-generated images. Because of this, I wanted all three categories to be represented equally. In the end, I decided to include eight images from each category, resulting in a total of 24 images.
I also wanted to avoid focusing on only one type of subject. If all images showed people, the results might only tell me how well participants recognise AI-generated faces. To get a broader picture, I chose images from different categories, including people, animals, landscapes and everyday scenes. This will hopefully help me see whether some types of images are easier to judge than others.
One category was especially important to me: AI-edited photographs.
Many studies and online discussions focus on images that have been created completely by AI. However, I think AI-edited images are just as interesting, if not even more interesting. In many cases, these images look almost identical to a real photograph because only a small part of the image has been changed. An object might have been added, removed or replaced, while everything else remains authentic. These small changes are often much harder to notice than a completely AI-generated image.
Besides the individual images, I also added a second part to my survey. In this section, participants compare two very similar images. One image is authentic, while the other has been edited using AI. Instead of deciding between four answer options, they simply have to choose which image they believe is the real one.
I included this second task because I am curious whether people perform differently when they can directly compare two images. Maybe small AI edits become easier to recognise when there is an authentic version right next to them, or maybe they are still difficult to spot. I am looking forward to finding out.
One final challenge was the survey platform itself. Google Forms reduces the image quality slightly, so very small details are not always visible. Because of this, I avoided choosing images where the answer depends on tiny artefacts or imperfections. Instead, I selected images that can still be judged even at a lower resolution.
Looking back, choosing the images took much longer than I expected, but it also made me think more deeply about my research question. Every image is now part of my experiment and plays an important role in helping me understand how people perceive authenticity in a world where AI-generated content is becoming more and more common.
