How does AI image detection work?

CheckReality runs multiple detection models simultaneously: AI generation detection identifies patterns left by generative models, deepfake detection analyzes facial regions for manipulation artifacts, metadata forensics checks EXIF data and compression signatures, and pixel-level analysis examines noise patterns and frequency domain anomalies. According to NIST research on media forensics (2024), ensemble detection methods outperform single-model approaches by reducing false positive rates by up to 40%.

Which AI generators can CheckReality detect?

CheckReality detects images from DALL-E, Midjourney (v5-v6.1), Stable Diffusion (1.5-3.0), Flux (Pro/Schnell/Dev), Adobe Firefly (2-3), Leonardo.ai, Runway Gen-2/Gen-3, Ideogram (1-2), Playground, Bing Image Creator, Google Imagen (2-3), and Canva AI, among 100+ other generators. Detection coverage is updated as new models release, with an average accuracy of 94% across tested generators per internal benchmarks.

How accurate is AI image detection?

CheckReality achieves 94%+ accuracy on common AI-generated images and 89%+ on advanced deepfakes, per internal benchmarks. The platform provides a confidence score with every analysis. Using multiple detection methods in parallel, the ensemble approach catches manipulations that any single model would miss. For high-stakes use cases, a certified expert review is available.

Is my uploaded image stored or shared?

Images are processed in memory and automatically deleted within 24 hours. We do not use your images for model training, share them with third parties, or retain any personally identifiable information. CheckReality is SOC 2 Type II certified and GDPR compliant, with AES-256 encryption at rest and TLS 1.3 in transit. EU and US data residency options are available for enterprise plans.

Can CheckReality detect manipulated images?

Yes. Beyond AI generation detection, CheckReality identifies image manipulation including face swaps, splicing, cloning, retouching, and metadata tampering through pixel-level inconsistency analysis. The platform uses 6+ forensic methods including error level analysis, JPEG ghost detection, and noise pattern comparison.

Do you provide AI expert witness testimony for court?

Yes. CheckReality provides certified AI expert witness services for litigation. Our deepfake forensic experts testify in depositions and at trial, providing Daubert-compliant expert reports on AI-generated images, deepfakes, and manipulated digital evidence. We also provide sworn affidavits and chain-of-custody audit trails. Expert witness engagements have supported cases in federal and state courts.

What is included in a deepfake forensic report?

Our AI forensic report for court includes GAN detection analysis, metadata forensics, pixel-level manipulation detection, frequency domain analysis, content credential (C2PA) verification, and a detailed methodology section. Reports meet federal evidence standards (Daubert v. Merrell Dow) and include chain-of-custody documentation. Expert witness testimony to support findings is available.

How can I use CheckReality for EU AI Act compliance?

Article 50 of the EU AI Act requires mandatory labeling of AI-generated content, with enforcement beginning August 2, 2026. CheckReality can scan your content library to identify unlabeled AI-generated images before the deadline. The platform generates audit reports suitable for regulatory submissions under the EU AI Act's transparency requirements.

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Compliance/March 15, 2026/8 min read/By CheckReality Team

EU AI Act Article 50: what enterprises need to know about transparency obligations

Article 50 of the EU AI Act requires labeling of AI-generated content. With enforcement approaching in August 2026, here's what organizations need to prepare.

Article 50 of the EU AI Act establishes transparency obligations for providers and deployers of AI systems. These provisions become enforceable on August 2, 2026, giving organizations a clear deadline to prepare.

What Article 50 requires

The regulation mandates transparency for AI-generated content:

AI-generated images must be labeled in a machine-readable format, including watermarks or metadata tags.
Deepfake content (AI-generated or manipulated image, audio, or video resembling existing persons or events) must carry a disclosure that it was artificially generated or manipulated.
Content providers must ensure technical solutions are effective, interoperable, and resistant to removal.

Non-compliance carries penalties of up to 3% of global annual revenue or EUR 15 million, whichever is higher, as specified in Article 99 of the EU AI Act.

Impact on enterprise workflows

For enterprises handling large volumes of visual content, compliance means every image entering your systems may need to be verified. The International Association of Privacy Professionals (IAPP) has identified several high-risk areas:

Marketing and advertising: Agencies using AI-generated visuals in campaigns must label them.
News and media: Publishers must verify and label AI-generated imagery before publication.
E-commerce: Product images generated or enhanced by AI must be disclosed.
[Insurance](/use-cases/insurance) and finance: AI-generated documents or images in claims and applications must be detected and flagged.

Building a compliance workflow

A practical approach involves three layers:

Intake scanning: Automatically scan all incoming visual content for AI generation markers.
Metadata verification: Check C2PA content credentials and EXIF data for provenance information.
Audit trail: Maintain a complete record of every detection result for regulatory review.

CheckReality provides all three layers in a single platform, with detection results that include the specific indicators found and confidence scoring suitable for compliance documentation.

The EU AI Pact, a voluntary initiative encouraging early adoption of AI Act provisions, has attracted significant interest from organizations preparing for compliance ahead of the enforcement deadline.

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