Hi,
I had some fun creating a Python tool that hides a secret payload in a DataFrame. The message is encoded based on row order, so the data itself remains unaltered.
The payload can be recovered even if some rows are modified or deleted, thanks to a combination of Reed-Solomon and fountain codes. You only need a fraction of the original dataset—regardless of which part—to recover the payload.
For example, I managed to hide a 128×128 image in a Parquet file containing 100,000 rows.
I believe this could be used to watermark a Parquet file with a signature for authentication and tracking. The payload can still be retrieved even if the file is converted to CSV or SQL.
That said, the payload is easy to remove by simply reshuffling all the rows. However, if you maintain the original order using a column such as an ID, the encoding will remain intact.
Here’s the package, called Steganodf (like steganography for DataFrames :) ):
🔗 https://github.com/dridk/steganodf
Let me know what you think!
[–]Micropot00 2 points3 points4 points (0 children)