For Researchers

Protect your research from synthetic contamination

With over three billion AI-generated images produced every month, dataset contamination is no longer a theoretical risk. DeepSight provides researchers with forensic verification tools to ensure the integrity of visual datasets and published findings.

Verify your dataset

3B+

AI-generated images produced per month

$1.72B

Projected AI image detection market by 2030

30+

Generative models detected


The Challenge

What researchers are up against

01

Dataset contamination

Training datasets scraped from the web now contain a growing proportion of AI-generated images. Without systematic detection, models trained on contaminated data inherit synthetic artifacts and produce unreliable results.

02

Reproducibility concerns

If a published study relies on a dataset containing undetected synthetic images, its findings may not be reproducible. Reviewers and replication teams increasingly scrutinize the provenance of visual data.

03

Peer review of synthetic data

Peer reviewers lack practical tools to verify whether figures, charts, or photographic evidence in submissions contain AI-generated components. Manual inspection is unreliable and does not scale.

04

Grant and publication credibility

Funding agencies and journals are beginning to require data provenance documentation. Researchers who cannot demonstrate the authenticity of their visual data risk rejection or retraction.


The Solution

How DeepSight helps

Batch dataset verification

Scan entire datasets for synthetic contamination. DeepSight identifies AI-generated images within large collections, flagging suspicious entries with confidence scores and generator attribution.

Forensic provenance reports

Generate detailed provenance reports for each image, including metadata analysis, statistical forensics, and source identification. These reports can be included as supplementary material in publications.

Multi-model detection

DeepSight detects output from 30+ generative models, including the latest diffusion architectures. The multi-signal approach avoids the blind spots that plague single-model detectors.

API for automated pipelines

Integrate detection into your data processing pipeline with our REST API. Automate verification during dataset curation, preprocessing, or quality assurance stages.


Your Workflow

How it works

1

Curate or receive a visual dataset for your research

2

Run images through DeepSight via upload or API

3

Review flagged images and confidence scores

4

Document provenance in your methodology section


Common Questions

Frequently asked

Yes. Our API supports batch processing, allowing you to submit datasets programmatically and receive results for each image. Enterprise plans include higher throughput and async processing via queue.


See it in action

Upload an image and watch the multi-signal cascade work — metadata, forensics, and semantic analysis in real time.

Verify your dataset