A pretrained if image is edited classifier that sorts an image into one of 2 categories. Use the if image is edited API immediately, no training required, then adapt it to your own data when you need more.
Drop in a photo and get the prediction back. No signup, no setup.
A sample of the 2 labels this pretrained classifier chooses between.
Need a label that isn't here? Clone the classifier into your Nyckel console and edit the label set to fit your data.
Once you've added this classifier to your console, you get your own copy of it behind your own endpoint. Invoke it with any HTTP client:
curl
curl -X POST "https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke" \
-H "Authorization: Bearer $NYCKEL_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{"data": "https://example.com/photo.jpg"}'Python
import requests
# Get an access token: https://www.nyckel.com/docs/api/overview/authentication/
token = "YOUR_ACCESS_TOKEN"
response = requests.post(
"https://www.nyckel.com/v1/functions/YOUR_FUNCTION_ID/invoke",
headers={"Authorization": "Bearer " + token},
json={"data": "https://example.com/photo.jpg"},
)
print(response.json())Example response
{
"labelName": "Edited",
"labelId": "label_...",
"confidence": 0.92
}Trained on a Nyckel-curated dataset covering 2 if image is edited categories, served on Nyckel's own infrastructure — your image stays on Nyckel.
Send an image URL or file to the invoke endpoint; the response is a label with a confidence score.
Clone it, then correct predictions and add your own samples in the console — Nyckel retrains automatically, turning this into a custom model tuned to your data.
This use case involves leveraging the image classification function to determine whether photos posted on social media have undergone edits. By identifying altered images, platforms can enhance their authenticity measures, combat misinformation, and provide users with clearer contexts around what they are viewing.
Law enforcement and legal teams can utilize the image editing identifier to verify the authenticity of photographic evidence presented in court. By detecting edited images, officials can better assess the integrity of evidence, strengthening their case or offering insights into potential tampering.
Online marketplaces can implement this technology to check product images submitted by sellers for any alterations. Ensuring that images are genuine helps maintain trust within the platform, reducing the potential for fraud and improving customer satisfaction.
News outlets can utilize the image editing identifier to authenticate images used in their reporting. By verifying whether an image has been altered, journalists can uphold ethical standards, maintain credibility with their audience, and reduce the spread of manipulated content.
Companies can employ this function to monitor online images associated with their brand across various platforms. Identifying edited images that misrepresent their products or services allows them to take proactive measures and protect their brand integrity.
Online platforms that host user-generated content can use the image editing identifier to filter out manipulated images that do not comply with community guidelines. This helps ensure a safer and more authentic user experience by minimizing the chances of users encountering deceptive or harmful content.
Museums and cultural institutions can implement this function to maintain the integrity of their digital collections. By identifying any alterations in archival images, they can ensure that historical records are preserved accurately, supporting research and educational purposes.
A zero-shot classifier uses a large foundation model's general knowledge to pick between your labels — no task-specific training, so new or edited labels work immediately. A Nyckel-trained classifier has been trained on labeled examples and runs on Nyckel's own infrastructure, which typically makes it faster, cheaper per call, and more accurate on data that resembles its training set. The "Under the hood" section on this page shows which kind this classifier is, and any classifier can be adapted into a trained one by adding your own examples.
Honestly: we can't know in advance — it depends on your data stream and how closely it resembles what this classifier has seen. The reliable way to find out is to measure it on your own data: start invoking the classifier with real traffic, or upload and annotate a set of images in the console — make sure they look like your production data, not idealized examples. Nyckel's evaluation metrics then show you exactly how it performs on that data before you rely on it.
No classifier is perfect, so Nyckel is built around the correction loop: invokes can be captured for review, you confirm or correct predictions in the console, and corrections become training data. Over time the model adapts to your data distribution — accuracy on your traffic improves with use rather than staying fixed.
No. This if image is edited classifier works out of the box — clone it into your console and you'll have your own API endpoint in under a minute. Training data only enters the picture when you want to adapt it: your corrected predictions and uploaded samples improve the model, and you can also edit the label set to match your needs.
Trying the classifier on this page is free with no signup. Cloning it requires a free account, and the free tier covers your first API calls each month — see nyckel.com/pricing for current limits and paid tiers.
Add this pretrained classifier to your Nyckel console — you'll get a live API endpoint in under a minute, and a path to a custom model when you need one.