TIFF with Metadata Stripped
The same losslessly compressed TIFF pixels with EXIF-style, XMP and IPTC metadata absent, providing a ground-truth scrubber control.
Specifications
- Metadata
- none (stripped)
- Pixel Sha256
- 68a5fadafa25d43cbe2f512f65dc2619dfe6558d3cc2f5583f849bfd7b5bb798
- Twin
- stripped
- Alt Text
- The same losslessly compressed TIFF pixels with EXIF-style, XMP and IPTC metadata absent, providing a ground-truth scrubber control
- Alt Text Source
- description
Testing contract
Expected to pass- Scenario
- Verify metadata removal from the stripped TIFF without changing its underlying content.
- Expected result
- Find no descriptive metadata; compute pixel SHA-256 68a5fadafa25d43cbe2f512f65dc2619dfe6558d3cc2f5583f849bfd7b5bb798, matching the full twin.
What is a .tiff file?
TIFF (Tagged Image File Format) is a flexible container that stores one or more raster images with a rich tag-based header describing layout, compression, and color. It supports uncompressed, LZW, and other codecs, high bit depths, multiple pages, and CMYK, making it common in publishing, scanning, and archival workflows. Its flexibility means reader support varies.
How to use this file
Use an example TIFF to test multi-page and high-bit-depth handling, tag parsing, and document-imaging or archival pipelines that must read varied compression and color models.
How to use this file for testing
“TIFF with Metadata Stripped” is a deterministic Testaroo fixture for Metadata testing, Conversion testing. Images, audio, video, and documents with documented metadata paired with deliberately stripped versions, for verifying extraction, preservation, redaction, and privacy-scrubbing behavior.
Documented properties for this file: TIFF · 1,514 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
For image AI or enhancement tools, run the model or filter on this file and diff against the clean or ground-truth companion in the same group when available. Keep seeds and documented damage parameters in your evaluation notes so regressions are attributable.
Code examples
<img src="tiff-stripped-metadata.tiff" alt="Example image" width="640" loading="lazy">Generated by generation/p6_core.py. Free for any use, no attribution required, license.
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