How do you use this tool?
- Click the upload area or drag and drop an image file (JPG, PNG, or WebP, up to 10 MB).
- The tool loads the ML model on first use (one-time download, ~5 MB, cached locally).
- Wait a few seconds while the classifier analyzes the image.
- Read the probability score: higher percentages indicate a stronger AI-generation signal.
- Review the confidence band — scores above 85% are high-confidence; 40–85% is uncertain territory.
What This Tool Does
As AI image generation has become mainstream, the need to quickly flag synthetic images has grown across journalism, education, social media moderation, and creative industries. This tool provides a fast, privacy-safe first-pass detection: upload an image and receive a probability score indicating how likely the image is AI-generated.
The classifier examines spectral artifacts, pixel statistics, and texture coherence — patterns that differ systematically between camera-captured images and the outputs of diffusion models or GANs.
How Does It Work?
Modern AI image detectors use binary classifiers trained on large datasets of real photographs and AI-generated images. The pipeline in this tool:
| Step | What Happens |
|---|---|
| Preprocessing | Image resized to 224×224, normalized to model input range |
| Inference | Neural network runs in your browser, accelerated by your GPU when available |
| Output | Softmax probability: P(AI-generated) and P(real photograph) |
| Display | Score + confidence band (low / uncertain / high) |
Confidence bands:
| Score Range | Interpretation |
|---|---|
| 0–40% | Likely a real photograph |
| 40–75% | Uncertain — borderline features present |
| 75–90% | Probable AI generation |
| 90–100% | High-confidence AI-generated signal |
What Are Common Use Cases?
- Social media verification: Check profile pictures or viral images before sharing to avoid spreading synthetic content.
- Editorial review: Journalists and photo editors screen submitted images for AI generation before publication.
- Academic integrity: Educators check student-submitted project images for AI-generated illustrations presented as original work.
- Brand safety: Marketing teams verify that user-generated content submitted to campaigns is authentic photography.
- Real estate and e-commerce: Platforms screen listing images to detect AI-generated property photos or product renders.
- Research and fact-checking: Newsrooms and fact-checkers use detection tools as the first step in image provenance workflows.
Frequently Asked Questions
Does this work on screenshots or memes? Screenshots often contain mixed content and UI chrome, which degrades accuracy significantly. The tool works best on full photographic images without overlaid text or interface elements.
Can it detect AI video frames? Upload individual frames extracted from a video as JPEG or PNG files. The tool analyzes still images only — video is not supported natively.
What is C2PA and why doesn’t this tool use it? C2PA (Coalition for Content Provenance and Authenticity) is a cryptographic metadata standard that records the origin of an image at creation time. Detection by pixel analysis is needed when C2PA metadata is absent, stripped, or not yet adopted by the generator. This tool uses pixel analysis — C2PA inspection is a complementary method available in other tools.
Will this keep working as AI models improve? Detection is an ongoing research challenge. As generative models improve, classifiers must be retrained on new outputs. The model used here is periodically updated, but very recent model releases may temporarily reduce accuracy.
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