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Novus Examples
png793 B

PNG with Metadata Stripped

The pixel-identical PNG control with text and ICC chunks removed, for proving that a scrubber changes metadata without changing decoded RGB values.

Preview of PNG with Metadata Stripped

Specifications

Metadata
none (stripped)
Pixel Sha256
68a5fadafa25d43cbe2f512f65dc2619dfe6558d3cc2f5583f849bfd7b5bb798
Twin
stripped

Testing contract

Expected to pass
Scenario
Verify metadata removal from the stripped PNG without changing its underlying content.
Expected result
Find no descriptive metadata; compute pixel SHA-256 68a5fadafa25d43cbe2f512f65dc2619dfe6558d3cc2f5583f849bfd7b5bb798, matching the full twin.

What is a .png file?

PNG (Portable Network Graphics) is a raster image format using lossless DEFLATE compression. It supports full 8- or 16-bit-per-channel truecolor, palette, and greyscale modes with an optional alpha channel, but no animation. It is the standard choice for screenshots, logos, and graphics with sharp edges or transparency.

How to use this file

Use an example PNG to test image decoders, alpha-compositing, thumbnail generators, and format converters, or to verify that a pipeline preserves transparency and color depth on round-trip.

How to use this file for testing

“PNG with Metadata Stripped” is a deterministic Novus Examples 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: PNG · 793 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="png-stripped-metadata.png" alt="Example image" width="640" loading="lazy">

Generated by generation/p6_core.py. Free for any use, no attribution required — license.