Oliver Weissbarth Software Developer, 3D-Graphics Enthusiast

Photo or Computer graphics: The survey evaluation

In the past decade computer graphics have reached a level of realism that makes it hard to distinguish them from actual photos. That is obviously pretty cool. The technological advancements have enabled artists to create amazing artwork and realize their creative vision. We are now as close as never before to visualize anything you can imagine.

However truly photo-realistic cg images also come with some risks. If you can’t tell a photo from a cg image that opens the door for all kinds of forgery and manipulation.

As part of my studies I am doing a project on “distinguishing between photos and computer graphics using neural networks”. I am building and training a machine learning model to classify input images as either photo or computer graphics. To evaluate the results in a meaningful way I needed something to compare against. Therefore i conducted a small study asking people to classify a selection of 49 photos and computer graphics images. The cg images were collected from Artstation.com while photos were found on Pixabay.com. I also collected some information about the participants’ background.

To reach a wider audience I used the opportunity to quickly present during the open-stage session at Blender Conference 2018. I described my project and asked for participation in the survey.

The study is now over and here are the results:

In total I got 2359 responses. The following is my evaluation of the survey. At the bottom of the page you can find links to the raw data licensed under ODC-BY, the evaluation scripts I used as well as the custom survey software.

How often do you use 3D-graphics-software (Blender, UE4, Modo, ZBrush, etc…)?

Possible answers were: Never, Once a month,  Once a week, Multiple times a week, Everyday.

The results show that the participants that have never used 3D-graphics-software are almost as well represented as users of 3D-graphics-software. This allows meaningful analysis of both groups.

Figure 1:Time spent using 3D-graphics-software per week

How much time do you spend per week playing computer/console games?

Possible answers were: less than 1h, 1-3h, 3-6h, 6-12h, more than 12h

Again participants that play many and few games are well represented.

Figure 2:Time spent playing video games per week

The interesting part

After those general questions the main part of the survey began. Each image was shown individually in a random order. Once the participant selected the category (cg or photo) the next image was shown. No results were shown in between to not influence the decision based on past results. A “back”-button was not provided.

The following shows each image, the correct label and the percentage of participants that classified it correctly. You can click on the images to enlarge them.

photo
author: olafpictures
title: Morocco door
95.33%
cg
author: Tomasz Żero
title: Mercedes-AMG S63
87.94%
cg
author: Christian Ledbetter
title: Reaching for petals
82.26%
photo
author: Oliver Weissbarth
title: Lake in the city
81.26%
photo
author: Engin_Akyurt
title: Coffee Table
80.14%
photo
author: Oliver Weissbarth
title: Sunflowers in the afternoon
79.79%
photo
author: Alexas_Fotos
title: Coffee Beans
77.56%
photo
author: Einladung_zum_Essen
title: ice dessert
77.55%
photo
author: Michael Gaida
title: Architecture Glass
77.49%
cg
author: Tomasz Żero
title: Jędrusik
77.42%
photo
author: Oliver Weissbarth
title: Clouds
77.21%
photo
author: Oliver Weissbarth
title: On the edge of the road
76.92%
cg
author: Krzysztof Pysz ONE2
title: Lennox Residence designed by Artau Architecture
75.41%
cg
author: Cristina Summa
title: Black and white Penthouse
73.77%
photo
author: khiemmoshe
title: Beautiful asian woman
73.7%
cg
author: Ivo Schoenmakers
title: Sitting corner
72.61%
cg
author: Alec Hunstad
title: Abandoned Clinic
72.46%
cg
author: Nicolas Delille
title: Ladybug Journey – Visual 2
72.27%
cg
author: Deniz Atli
title: Forest House
72.25%
cg
author: Enrico Cerica
title: Industrial loft with scandinavian style
71.83%
photo
author: HOerwin56
title: Wireless headphones
71.62%
cg
author: DennisN / A33SOME CGI Studio
title: Notting Hill Westbourne Grove Apartment
67.07%
cg
author: Andrew Finch
title: Control Panel
63.19%
photo
author: MabelAmber
title: Piano
62.75%
photo
author: ivanovgood
title: Model Girl
61.66%
photo
author: jodeng
title: Road
60.98%
cg
author: Tuna Unalan
title: Raspberry Madness
59.68%
cg
author: Enrico Cerica
title: Green House 2018
59.06%
photo
author: Skitterphoto
title: Modern Kitchen
59.06%
photo
author: monicore
title: Tomato
58.34%
cg
author: Bertrand Benoit
title: Classical
57.55%
photo
author: Pexels
title: Bottle caps beer
57.07%
photo
author: Erika Wittlieb
title: Bedroom furniture
56.87%
photo
author: JerzyGorecki
title: Woman
56.51%
photo
author: StockSnap
title: Kitchen interior
53.58%
cg
author: Marko Ivanovic
title: Heeeeeere’s Johnny!
52.88%
cg
author: Ivo Schoenmakers
title: Towel Photorealism
52.82%
photo
author: Lars_Nissen_Photoart
title: Telescope
50.77%
photo
author: Oliver Weissbarth
title: The kings head
49.06%
cg
author: Tomasz Żero
title: Jędrusik
46.96%
cg
author: Eoin O’Broin
title: UE4 Scenes
46.94%
cg
author: Juan Siquier
title: WC
46.04%
cg
author: Bertrand Benoit
title: Hooper
44.12%
cg
author: Bertrand Benoit
title: Classical
43.93%
cg
author: Bertrand Benoit
title: Hooper
40.14%
cg
author: Eoin O’Broin
title: UE4 Scenes
37.11%
photo
author: obBilder
title: E guitar
30.46%
photo
author: Yummymoon
title: Glass and Bottle
29.16%
cg
author: Trevor Curtis
title: MxSS Keyboard renders
25.83%

Overall 62.40% of the images were classified correctly. The participant with the best result classified 100% of the images correctly while the minimum was 26.53%. As 25 out of the 49 images are cg images the expected result with random guessing is 50%. Figure 3 shows a histogram of the accuracy over all participants.

Figure 3: A histogram over the accuracy of all participants

Figure 4 shows the accuracy based on the time spent using 3D-graphics software. It can be seen that people who use 3D-graphics software on a daily basis classified the images about 8% more accurately than people who have never used 3D-graphics software. This might suggests that the ability to distinguish between photos and cg images is to some extent learnable.

Figure 4:Average accuracy by 3d-graphics-software usage

Plotting the accuracy based on the time spent playing video games does not show an obvious correlation (Figure 5).

Figure 5:Average accuracy by video game consumption

Another interesting evaluation is to see if participants tend to wrongly classify cg as photo or photo as cg. Figure 6 shows the classification accuracy separately for photo and cg images.

CG

photo

Figure 6:Accuracy per class

It can be seen that participants were slightly better at classifying the photos but not by a significant margin.

For each image the time it took to decide was also stored. As the survey was not done in a controlled environment these results should be taken with a grain of salt.

That said Figure 7 shows the accuracy based on the average time spent per image. The chart is limited to 120s. All higher values have been cut off. It can be seen that neither deciding instantly nor taken too long yields better results.

Figure 7: Time spent averaged over all images for each participant


Final thoughts

Distinguishing between photos and cg images can be pretty difficult. The result of only 58.67% accuracy for the non-3D-graphics-software users suggests that for the untrained eye it is basically impossible.

Regular users 3D-graphics-software got better results than participants that had never used 3D-graphics-software. It would be interesting to find out whether this is specific to 3D-graphics-software or applies to other visual/artistic tasks like painting.

Something that was not covered by this study is the resolution and size at which the images were shown. It is likely that part of the participants viewed the images on a small mobile device and part on a larger screen. It is possible (even likely) that this also influences the results.

The more time you spend with the data the more creative ideas come to mind. And there is a lot more to be discovered.

After the survey had opened I started working on a machine learning model to distinguish photo from cg. While some initial results look promising there are still improvements to make. So stay tuned for the results of my neural network and a full comparison.

Links

The raw data

The data was collected between the October  25th 2018 and January 16th 2019. It is licensed under the Open Database License with Attribution (ODC-By). For more information see the attached license.txt

Details about the schema can be found in schema.txt

The images are not part of the data-set and not licensed under ODC-By. Instead all rights belong to the respective authors. All images were used with permission.

Photo or CG Survey Raw data (17 downloads)

Evaluation scripts

The scripts used to generate the above evaluations can be found here.

Survey software

The software that was used to run the survey is custom and can be found here. This was developed more or less in a single day. It is very purpose-built (read hacky)

Leave a Reply

Your email address will not be published. Required fields are marked *