Forensic Cleaner by Two Tensors
Image cleanup tool for reducing AI-detector signals, removing watermarks, and stripping hidden metadata.
Product Overview
What is Forensic Cleaner by Two Tensors?
Forensic Cleaner by Two Tensors helps creators and businesses process images that they own or have permission to use. Users can upload an image, validate its quality and detector risk, reduce detector-facing pixel signals, remove Gemini and SynthID watermarks, strip metadata, and export a cleaned version. The service is available through a web app and API, with results varying by image and third-party detector.
Key Features
AI Signal Reduction
Reduces image patterns that may trigger automated AI-content detectors while keeping the output close to the original.
Watermark Removal
Removes visible Gemini logos and processes invisible SynthID watermarks with adjustable strength settings.
Metadata and C2PA Cleanup
Strips EXIF, IPTC, XMP, C2PA manifests, Content Credentials, and other embedded provenance data.
Validation and Comparison
Checks image dimensions and safety before processing and supports before-and-after comparisons.
Web and API Workflows
Supports browser-based processing and programmatic workflows for uploading, monitoring, and downloading cleaning jobs.
Adjustable Processing Modes
Offers Strong mode for more aggressive cleanup and Quality mode for gentler processing with fewer visible changes.
Use Cases
- Image Publishing : Creators can prepare owned images for websites, portfolios, marketplaces, and social platforms where automated AI screening is used.
- Digital Asset Management : Teams can remove embedded metadata and content credentials before storing, transferring, or repurposing image assets.
- Creative Review : Designers and editors can compare original and processed images to evaluate cleanup effects and visual quality.
- Content Operations : Marketers and publishers can process image assets through a repeatable web or API-based workflow.
- Research and Testing : Researchers can study how different detectors respond to image transformations without relying on a single detection system.
FAQs
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