机器学习检测

ML detection uses trained neural networks (specifically Vision Transformers) to analyze images and identify patterns characteristic of AI-generated content, such as those from Stable Diffusion, DALL-E, or Midjourney.

ML detection is our most accurate single method, trained on millions of images. It achieves 92-98% accuracy on direct AI outputs, making it the primary signal in our ensemble detection system.

Our ML model detects images from Stable Diffusion (all versions), DALL-E 2 & 3, Midjourney, Adobe Firefly, Leonardo.ai, and most diffusion-based generators.

Vision Transformers (ViT) divide images into patches and learn attention patterns between them. They can identify subtle correlations that differ between AI-generated and real photographs.

Moderate JPEG compression (quality 60-100) has minimal impact. Heavy compression or multiple re-compressions can reduce accuracy, which is why we use ensemble methods.

We support JPEG, PNG, WebP, BMP, and TIFF formats. All images are preprocessed to 224x224 pixels while preserving aspect ratio for optimal model inference.

Our CPU-optimized model processes images in under 500ms. GPU acceleration can reduce this to under 50ms for batch processing.

ML detection focuses on fully AI-generated images. For AI-enhanced or partially edited photos, other methods like clone detection work better.

Yes, we regularly retrain our models to include outputs from the latest AI image generators. The current model is trained on images from 2024-2026 generators.

Our ML model has a false positive rate under 2% for standard photographs. Heavily filtered or stylized photos may occasionally trigger false positives, which ensemble methods help mitigate.