TurboLens

以下为收录时的原文，语言与页面不同，未做翻译。

TurboLens is an OCR tool that extracts and translates text from images, including multi-language support and recognition of mathematical formulas and tables. Its workflow management features enhance efficiency for processing both printed and handwritten documents.

官网介绍

Detect tampered, AI-generated, and AI-edited images with TurboLens visual forensics APIs, then extract structured data with specialized Southeast Asian OCR.

官网要点

• Forgery detection
• AI image detection
• Integration process
• AI Image Forgery & Synthetic Media Detection
• Compare image forgery and AI image detection
• Image Forgery Detection

官方常见问题

What is image forgery detection?

Image forgery detection analyzes an existing image for evidence of local manipulation, such as splicing, copy-move edits, retouching, or altered fields. TurboLens returns a real-or-forged verdict, the proportion of suspicious pixels, region coordinates, and a heatmap that helps reviewers see where an edit may have occurred.

How is AI-generated image detection different from forgery detection?

Forgery detection looks for edits made to an existing image, while AI-generated image detection estimates whether generative AI created or modified the image. These are separate forensic questions, so TurboLens treats them as two complementary screening layers rather than one interchangeable score.

Can TurboLens detect AI-edited images as well as fully generated images?

The synthetic-media service is being developed to screen both fully AI-generated images and images changed with generative editing tools. It is currently offered as early access, with a three-state result and probability score designed to support—not replace—human review and business rules.

Where does visual forgery detection fit in a document workflow?

Most teams place visual forensics immediately after upload and before OCR, extraction, or approval. Clean submissions continue through the existing pipeline. Flagged images can be rejected, escalated, or sent to a manual-review queue according to the organization’s own thresholds and risk policy.