Human vs. AI : A Novel Benchmark and a Comparative Study on the Detection of Generated Images and the Impact of Prompts (2025)

Abstract: With the advent of publicly available AI-based text-to-image systems, the process of creating photorealistic but fully synthetic images has been largely democratized. This can pose a threat to the public through a simplified spread of disinformation. Machine detectors and human media expertise can help to differentiate between AI-generated (fake) and real images and counteract this danger. Although AI generation models are highly prompt-dependent, the impact of the prompt on the fake detection performance has rarely been investigated yet. This work therefore examines the influence of the prompt’s level of detail on the detectability of fake images, both with an AI detector and in a user study. For this purpose, we create a novel dataset, COCOXGEN, which consists of real photos from the COCO dataset as well as images generated with SDXL and Fooocus using prompts of two standardized lengths. Our user study with 200 participants shows that images generated with longer, more detailed prompts are detected significantly more easily than those generated with short prompts. Similarly, an AI-based detection model achieves better performance on images generated with longer prompts. However, humans and AI models seem to pay attention to different details, as we show in a heat map analysis.

## introduction state-of-the-art ai-based image generators, such as dall-e @xcite , midjourney (midjourney, inc., 2024) or stable diffusion @xcite have the ability to create photorealistic images in a fully synthetic manner @xcite . the fact that these systems are publicly available makes them contribute to the fast spread of synthetic image content on the internet. this increases the threat of disinformation @xcite . thus, there is a need for reliable detection of ai-generated images. although it is well known that image generation is highly dependent on the textual prompt @xcite , the impact of the prompt on the fake detection performance has been rarely investigated yet. an exception is the work by @xcite who analyze prompts regarding their topic and structure. in particular, the level of detail in the prompt might affect the number of artifacts as more details might force the model to generate an image that deviates more from its training data. therefore, we pose the question whether the level of detail in the prompt has an impact on the ability of humans and ai-models to detect generated images. existing research on fake image detection shows that average human performance is not substantially better than chance @xcite . other works show that machine detection performance can be considered rather reliable, at least for images generated by models that have been included in the training data of detection systems @xcite : our contributions are (1) the creation of cocoxgen, a novel benchmark dataset with images created by two different generation models based on prompts of two different levels of detail, (2) the conduction of a large-scale user study on fake image detection, and (3) a direct comparison of human and machine detection performance and decision rationales. some works also investigate which aspects of the input lead to the detection decisions: ai models, on the one hand, tend to involve larger image areas in their decision on real images than on synthetic ones @xcite . this leads to the impression that the detection of synthetic images is focused on fine details while the detection of real images is focused on more abstract contents. humans, on the other hand, tend to pay attention to specific objects as well as to their general impression of the image @xcite . again, it is not possible to directly compare the strategies of humans and ai-models due to different evaluation setups. thus, we also address the open research question whether humans and ai models consider the same objects and structures in an image when evaluating it as real or fake. to address our research questions, we present cocoxgen, a new dataset containing real photos from the coco dataset @xcite and ai-generated images from sdxl @xcite and fooocus (lllyasviel, 2024) with prompts of two different levels of detail. our dataset is publicly available foot_0 and can be used in future work as a benchmark dataset for evaluating fake image detection performance. we conduct a user study with 200 human participants and evaluate the machinelearning classifier grag2021 @xcite on our new dataset to be able to directly compare the detection performance (f1 scores) of humans and a state-of-the-art ai model. in our analysis, we visualize image areas that lead to the decisions of humans and machine detectors in a comparable heat map structure to investigate both qualitatively and quantitatively whether humans and ai models pursue similar strategies. ## dataset to the best of our knowledge, there is no dataset publicly available that contains real images and aigenerated images from prompts with a controllable level of detail. therefore, we create and publish cocoxgen (coco extended with generated images), a novel benchmark dataset for the evaluation of fake image detection performance. ## user study with this study, we measure human classification performance (in terms of f1 score) for aigenerated and real images. moreover, we investigate the impact of the level of detail in the prompts on the human performance. we assume that a more detailed prompt, i.e., a more complex demand, leads to a higher chance of artifacts in the generated images as the generation model needs to deviate more from its training data to fulfill the individual request. therefore we state the following hypothesis: h 1 : humans achieve higher detection performance for images generated with longer, more detailed prompts, than for those generated with short prompts. our datasets, which was created using two different state-of-the-art image generation models, further allows us to test human performance per generation model, i.e., to investigate which model creates the most photorealistic images from human perspective. as we assume that fooocus images are more photorealistic due to its task-specific refiner, we state the following hypothesis: h 2 : humans achieve higher detection performance for sdxl images than for fooocus images. in addition to investigating pure performance, we further analyze which part of the image leads to the decision of the participants when classifying a photograph as real or fake as well as how certain they are in doing so. finally, we analyze whether the participants' experience with ai-generated images before the study influences their detection performance. ## machine classification performance for automatically distinguishing real from aigenerated images, we apply grag2021 @xcite , a resnet50 @xcite which is one of the most popular architectures for image classification. we use the trained and publicly available version by @xcite . diffusion images which fits our dataset well. ## discussion this section discusses the implications of the most important findings of this work. ## conclusion and future work in this work, we investigated the influence of the prompt's level of detail for distinguishing aigenerated images from real ones. in particular, we explored both the performance of humans and a machine detector and directly compared their decision rationales. for this purpose, we created a novel benchmark dataset cocoxgen, which contains ai-generated sdxl and fooocus images created with prompts of two different levels of detail as well as real coco images. we found that images generated with prompts with more details can be recognized more easily as fake than images generated with short prompts with less details. this observation holds for both humans and the machine detector although their decision rationales show only a low correlation. future work can expand our study by investigating more types of generation and machine detection models. in addition, additional aspects of prompt composition (e.g., the number of words per partof-speech class) and their impact on fake image detection performance can be explored.