Is this real?
Substantial Evidence of Manipulation
TrueMedia.org verdict: substantial evidence of manipulation.
Verified by human analyst
Analysis
Results
Generative AI
Detects signatures of GenAI tools
Substantial Evidence
Visual Noise
Variations in pixels and color
Uncertain
Disclaimer: TrueMedia.org uses both leading vendors and state-of-the-art academic AI methods. However, errors can occur.
AI-Generated Insights
Our detector analyzed the media for signs of digital manipulation and noted the following insights. This AI-generated explanation is provided for additional context and is only one component of our analysis.
The image appears to have characteristics often seen in AI-generated pictures or digital manipulations, such as unnatural skin textures and unrealistic detailing around the hair and facial features.
Generative AI5
Detects signatures of GenAI tools
Substantial Evidence
Multi-Modal AI
Analyzes the image for indications it was generated by AI or otherwise digitally manipulated.
Substantial Evidence
100% confidence AI Generated Image Detection
MidJourney
Likely source
The model was trained on a large dataset comprising millions of artificially generated images and human-created images such as photographs, digital and traditional art, and memes sourced from across the web.
Substantial Evidence
97% confidence Image Generator Analysis
MidJourney
Likely source
Analyzes image for indications that it was generated by popular AI image generators, like MidJourney, Dall-E, Stable Diffusion and thispersondoesnotexist.com.
Substantial Evidence
81% confidence Universal Fake Detector Analysis
Using the feature space of a large, pretrained vision-language model, this model analyzes images to determine if they were generated by a variety of popular generative and autoregressive models.
Little Evidence
AI Generated Image Detection
Detects AI-generated photo-realistic images, created for example by Generative Adversarial Networks and Diffusion Models like Stable Diffusion, MidJourney, DALL·E 2 and others.
Visual Noise1
Variations in pixels and color
Substantial Evidence
91% confidence Diffusion-Generated Image Detection
Evaluates the discrepancy between the original image and the version reconstructed by pre-trained diffusion models. Such models are known to potentially capture visual noise, commonly associated with the diffusion process.
