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Autotrained Spoof Detector
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Autotrained Spoof Detector

Developed by venuv62
This is a binary classification model trained via AutoTrain to distinguish between real or fake images.
Image Classification
Transformers
#Real-Fake Detection#Image Authenticity Verification#Binary Classification
Downloads 27
Release Time : 12/19/2022

Model Overview

The model specializes in image classification tasks, capable of identifying whether an image is real or fake. Suitable for scenarios such as content moderation and image authenticity verification.

Model Features

Auto-Training
Uses AutoTrain to automatically optimize the training process without manual hyperparameter configuration.
Lightweight
CO2 emissions of only 2.25 grams, indicating a relatively eco-friendly training process.
Balanced Performance
Achieves a good balance between accuracy, recall, and F1 score.

Model Capabilities

Image Classification
Authenticity Detection
Binary Decision

Use Cases

Content Moderation
Fake Image Detection
Identify forged or manipulated images on social media.
Accuracy 73%, can effectively filter some fake content.
Digital Forensics
Image Authenticity Verification
Verify the authenticity of news images or evidence materials.
Recall 76%, can effectively detect forged images.
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