Description
< #Blind Watermark #Image Processing #Copyright Protection #Content Tracing #Python #Open Source
blind_watermark embeds hidden information into an image without any visible change, and extracting the watermark needs no original image — which is exactly what the "blind" in blind watermarking means. It works in the frequency domain with DWT-DCT-SVD, so the payload lives in the image's frequency components rather than as a translucent overlay on the picture.
It fits copyright marking, content provenance, and tracing the source of a leak. A visible watermark can be cropped or painted over; a blind one cannot be removed without damaging the image itself, and it survives cropping, rotation, resizing, masking, noise and brightness changes well enough to still be recovered.
Extraction without the original: given the password used at embedding time and the watermark length or shape, the mark can be read straight out of a leaked copy — no reference image required.
Three payload types: text, an image, or a plain array of bits, covering a signature, a logo, and custom-encoded tracking identifiers respectively.
Robust to common edits: the project documents a full set of attacks — 45-degree rotation, random crop, multiple masks, vertical and horizontal cuts, resize, salt-and-pepper noise and a 10% brightness drop — each with the watermark that was recovered afterwards.
Two passwords: the image and the watermark each take their own password, so knowing which library was used is not enough to read the payload back.
Command line ready: installation provides a `blind_watermark` command, with one `--embed` call to mark an image and one `--extract` call to read it back, no code required.
A few lines to integrate: in Python it is read_img, read_wm and embed, with a single extract call on the way back, which drops easily into an existing image pipeline.
Parallel processing: pass a processes argument to run the work across cores, defaulting to all available processes so large images do not stall the pipeline.
Pure Python: install with `pip install blind-watermark`, runs on Python 3.5 and above across Windows, Linux and macOS, with no extra system dependencies.
Open source project: the source code is hosted on GitHub, so developers can review the implementation, contribute, or customize it for their own needs.
blind_watermark embeds hidden information into an image without any visible change, and extracting the watermark needs no original image — which is exactly what the "blind" in blind watermarking means. It works in the frequency domain with DWT-DCT-SVD, so the payload lives in the image's frequency components rather than as a translucent overlay on the picture.
It fits copyright marking, content provenance, and tracing the source of a leak. A visible watermark can be cropped or painted over; a blind one cannot be removed without damaging the image itself, and it survives cropping, rotation, resizing, masking, noise and brightness changes well enough to still be recovered.
Features
Extraction without the original: given the password used at embedding time and the watermark length or shape, the mark can be read straight out of a leaked copy — no reference image required.
Three payload types: text, an image, or a plain array of bits, covering a signature, a logo, and custom-encoded tracking identifiers respectively.
Robust to common edits: the project documents a full set of attacks — 45-degree rotation, random crop, multiple masks, vertical and horizontal cuts, resize, salt-and-pepper noise and a 10% brightness drop — each with the watermark that was recovered afterwards.
Two passwords: the image and the watermark each take their own password, so knowing which library was used is not enough to read the payload back.
Command line ready: installation provides a `blind_watermark` command, with one `--embed` call to mark an image and one `--extract` call to read it back, no code required.
A few lines to integrate: in Python it is read_img, read_wm and embed, with a single extract call on the way back, which drops easily into an existing image pipeline.
Parallel processing: pass a processes argument to run the work across cores, defaulting to all available processes so large images do not stall the pipeline.
Pure Python: install with `pip install blind-watermark`, runs on Python 3.5 and above across Windows, Linux and macOS, with no extra system dependencies.
Open source project: the source code is hosted on GitHub, so developers can review the implementation, contribute, or customize it for their own needs.
