Introduction
In this article, we will evaluate and identify AI-generated images using Hive Moderation AI-GENERATED CONTENT DETECTION.
The AI image generation tools we’ll focus on are:
Premise
In the world of generative AI, Content Credentials are becoming increasingly prevalent.
With the improved quality of content from generative AI models, the need for transparency regarding the origin of AI-generated content has grown. All AI-generated images from Azure OpenAI Service now include Content Credentials—a tamper-evident method to disclose the origin and history of content. Content Credentials are based on an open technical specification from the Coalition for Content Provenance and Authenticity (C2PA), a Joint Development Foundation project.
Reference: Content Credentials in Azure OpenAI - Microsoft Learn
Some of the images used in this evaluation include Content Credentials, yet there were many cases where the detection results didn’t align perfectly. For instance, images generated on the web using Firefly by Adobe do include Content Credentials but were still identified as having a low probability of being AI-generated.
Because of this, we will proceed without considering Content Credentials in this article.
Additionally, while this article only includes two to three images per tool, more were tested in practice. Since the trends were consistent, we are presenting only a selection here.
DALL·E 3
We first evaluated images generated by DALL·E 3 from OpenAI, created by providing prompts through ChatGPT.
Although some of the labels were inaccurate, all images were correctly identified as AI-generated with a high probability score of over 99%. This is quite accurate, as they do look like AI-generated images.
On a related note, I’ve recently heard people say, "This image looks AI-generated." I wonder what criteria people use to make that judgment.
FLUX.1[dev]
Next, we evaluated images generated using FLUX.1[dev] by Black Forest Labs.
As expected, these were also identified as AI-generated with a high probability exceeding 99%.
ImageFX
Next, we tested images generated by Google’s ImageFX, which has gained attention for its impressive realism.
The first image scored slightly below 90%, but it was still detected as AI-generated with a high probability.
On a side note, the quality of the photo-like images, especially of people, is truly impressive and lives up to the hype. It’s becoming harder to believe that these images are AI-generated without prior knowledge.
Stable Diffusion
Next, we evaluated images generated by Stable Diffusion from Stability AI.
Both images were detected as AI-generated, as expected.
Testing Image to Image
Since almost all images so far were detected as AI-generated with over 99% accuracy, I decided to try something different.
With Image to Image generation, you can specify a base image for the AI to modify. I’ll test how well this detection tool identifies AI modifications.
Here’s a photo I took at a certain location. I’ve reduced the file size for easier viewing.
Using the same Stable Diffusion 3 Large, I applied the prompt "Night view of buildings" with Image to Image generation. The degree to which the prompt modifies the original image can be adjusted between 0.0 and 1.0, with higher values deviating more from the original image. I generated images at various stages of modification and tested them.
Original Image
Detection result: near 0.
strength 0.3 (original image 0.7)
Probability increased by only 0.2%, still near 0.
strength 0.5 (original image 0.5)
Now entering double digits.
strength 0.7 (original image 0.3)
Approaching 50%.
strength 0.9 (original image 0.1)
Surpassed 50%.
As the degree of modification increases, the probability of AI detection rises proportionally.
Firefly
Lastly, we tested Firefly from Adobe, with a slightly different approach.
Modifying the Sky
Using Photoshop, I applied AI-generated content focused on the sky in the previous night view image. Here’s the result. Only the upper half of the image is AI-generated, while the lower half remains unchanged from the original.
The detection result is as follows.
Detection result: 0.1%.
Modifying the River
Next, I left the upper half as-is and applied AI generation to the lower half of the image.
The detection result is as follows.
Detection result: similarly low probability.
Testing 100% AI-Generated Image
Finally, I tested an entirely AI-generated image from Firefly using a prompt.
The detection result is as follows.
Detection result: slightly above 1%, but still relatively low.
While Firefly is listed as a supported tool in AI-GENERATED CONTENT DETECTION, could it be that it does not yet fully account for the latest generation logic? Alternatively, could Firefly operate on fundamentally different principles?
Conclusion
While this tool provides a useful benchmark, it remains difficult to accurately detect AI-generated content in every case.
As I’ve noted previously, with the prevalence of AI in digital tools today, attempting to definitively distinguish between AI-generated and non-AI content may be a futile effort.
Japanese Version of the Article
AI生成かどうかを判定する「Hive Moderation AI-GENERATED CONTENT DETECTION」にDALL·E 3やFLUX.1、ImageFX等の生成画像を判定させてみた
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