AI vs Human Image Detector
| Property | Value |
|---|---|
| Author | Ateeqq |
| Framework | PyTorch + Transformers |
| Model Architecture | SIGLIP Classification |
| Model Link | Hugging Face |
What is ai-vs-human-image-detector?
The ai-vs-human-image-detector is a specialized binary classification model designed to differentiate between AI-generated and human-captured images. Built on the SIGLIP architecture and implemented using the Transformers library, this model demonstrates exceptional accuracy with confidence scores reaching up to 99.96% in test cases.
Implementation Details
The model leverages the SiglipForImageClassification architecture and is optimized for both CPU and GPU environments. It utilizes automatic image processing through the AutoImageProcessor class, ensuring consistent input preparation regardless of source image characteristics.
- Supports RGB image input with automatic conversion
- Implements torch.no_grad() for efficient inference
- Features softmax probability output for classification confidence
- Includes built-in preprocessing pipeline
Core Capabilities
- Binary classification between AI-generated ('ai') and human-captured ('hum') images
- High-confidence predictions with probability scores
- Efficient batch processing support
- Cross-platform compatibility (CPU/GPU)
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in the increasingly important task of distinguishing AI-generated images from human-captured photos, achieving remarkably high confidence scores and implementing the advanced SIGLIP architecture.
Q: What are the recommended use cases?
The model is ideal for content moderation, digital forensics, authentication of photographs, and verification of image sources in media and publishing contexts.
