Identify editing quality
using AI
Below is a free classifier to identify editing quality. Just upload your image, and our AI will predict the editing quality of your images - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("editing-quality", "your_image_url", credentials)
fetch('https://www.nyckel.com/v1/functions/editing-quality/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_image_url"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_image_url"}' \
https://www.nyckel.com/v1/functions/editing-quality/invoke
How this classifier works
To start, upload your image. Our AI tool will then predict the editing quality of your images.
This pretrained image model uses a Nyckel-created dataset and has 44 labels, including Hdr, Amateur, Artisanal, Artistic Style, Black And White, Cinematic, Color Graded, Commercial, Creative Edits and Detail-Oriented.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the editing quality of your images).
Whether you're just curious or building editing quality detection into your application, we hope our classifier proves helpful.
Need to identify editing quality at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Content Moderation: This function can assist social media platforms by identifying edited images that may have been altered to misrepresent reality. By flagging these images, platforms can ensure compliance with community guidelines and maintain a trustworthy user environment.
- E-commerce Verification: Online retailers can use this identifier to verify the authenticity of product images submitted by sellers. By detecting edited images, platforms can reduce fraudulent listings and enhance consumer confidence in their marketplace.
- Digital Forensics: Investigators can employ this function to assess digital evidence in legal cases. By identifying false images, they can determine the integrity of visual content and potentially uncover additional layers of manipulation in evidence.
- News Integrity Assurance: News organizations can integrate this function to verify the authenticity of images used in their reporting. With the rise of misinformation, ensuring that published images are unaltered helps maintain journalistic credibility.
- Social Media Influence Analysis: Brands can utilize this identifier to analyze images posted by influencers, assessing whether edited images align with their marketing transparency policies. This can help brands collaborate with influencers who maintain genuine representation.
- Art and Photography Authenticity: Galleries and auction houses can employ this function to verify the authenticity of artwork images. By detecting edits, they can avoid selling misrepresented or digitally altered pieces, protecting their reputation and clientele.
- Academic Research: Researchers studying visual culture and digital media can leverage this function in their investigations. By identifying and cataloging edited images, they can draw conclusions about societal trends in image manipulation and digital representation ethics.