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https://github.com/ShieldMnt/invisible-watermark
python library for invisible image watermark (blind image watermark)
https://github.com/ShieldMnt/invisible-watermark
blind-watermark image-watermark
Last synced: about 1 month ago
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python library for invisible image watermark (blind image watermark)
- Host: GitHub
- URL: https://github.com/ShieldMnt/invisible-watermark
- Owner: ShieldMnt
- License: mit
- Created: 2021-01-30T15:21:18.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-23T05:49:41.000Z (12 months ago)
- Last Synced: 2024-07-10T08:44:08.624Z (2 months ago)
- Topics: blind-watermark, image-watermark
- Language: Python
- Homepage:
- Size: 5.56 MB
- Stars: 1,520
- Watchers: 15
- Forks: 138
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# invisible-watermark
[](https://pypi.org/project/invisible-watermark/)
[](https://github.com/ShieldMnt/invisible-watermark/blob/main/LICENSE)
[](https://pepy.tech/project/invisible-watermark)
invisible-watermark is a **python** library and command line tool for creating invisible watermark over image (a.k.a. **blink image watermark**, **digital image watermark**). The algorithm doesn't rely on the original image.
**Note that** this library is still experimental and it doesn't support GPU acceleration, carefully deploy it on the production environment. The default method **dwtDCT**(one variant of frequency methods) is ready for on-the-fly embedding, the other methods are too slow on a CPU only environment.
[supported algorithms](https://github.com/ShieldMnt/invisible-watermark#supported-algorithms)
* [Discrete wavelet transform](https://en.wikipedia.org/wiki/Discrete_wavelet_transform) + [Discrete cosine transform](https://en.wikipedia.org/wiki/Discrete_cosine_transform) frequency embedding algorithm variants.
* [RivaGAN](https://github.com/DAI-Lab/RivaGAN), a deep-learning model trained from Hollywood2 movie clips dataset.
[speed](https://github.com/ShieldMnt/invisible-watermark#running-speed-cpu-only)
* default embedding method ```dwtDct``` is fast and suitable for on-the-fly embedding
* ```dwtDctSvd``` is 3x slower and ```rivaGan``` is 10x slower, for large image they are not suitable for on-the-fly embedding
accuracy
* The algorithm **cannot guarantee** to decode the original watermarks 100% accurately even though we don't apply any attack.
* Known defects: Test shows all algorithms do not perform well for web page screenshots or posters with homogenous background color
## Supported Algorithms
* [**frequency methods**](https://github.com/ShieldMnt/invisible-watermark/wiki/Frequency-Methods)
> * **dwtDct**: DWT + DCT transform, embed watermark bit into max non-trivial coefficient of block dct coefficents
>
> * **dwtDctSvd**: DWT + DCT transform, SVD decomposition of each block, embed watermark bit into singular value decomposition
* [**rivaGan**](https://github.com/ShieldMnt/invisible-watermark#rivagan-experimental): encoder/decoder model with Attention mechanism + embed watermark bits into vector.
> background:
> * [Discrete wavelet transform](https://en.wikipedia.org/wiki/Discrete_wavelet_transform)
> * [Discrete cosine transform](https://en.wikipedia.org/wiki/Discrete_cosine_transform).
> * [RivaGAN](https://github.com/DAI-Lab/RivaGAN), a deep-learning model trained from Hollywood2 movie clips dataset.
## How to install
`pip install invisible-watermark`
## [Library API](https://github.com/ShieldMnt/invisible-watermark/wiki/API)
### Embed watermark
* **example** embed 4 characters (32 bits) watermark
```python
import cv2
from imwatermark import WatermarkEncoder
bgr = cv2.imread('test.png')
wm = 'test'
encoder = WatermarkEncoder()
encoder.set_watermark('bytes', wm.encode('utf-8'))
bgr_encoded = encoder.encode(bgr, 'dwtDct')
cv2.imwrite('test_wm.png', bgr_encoded)
```
### Decode watermark
* **example** decode 4 characters (32 bits) watermark
```python
import cv2
from imwatermark import WatermarkDecoder
bgr = cv2.imread('test_wm.png')
decoder = WatermarkDecoder('bytes', 32)
watermark = decoder.decode(bgr, 'dwtDct')
print(watermark.decode('utf-8'))
```
## CLI Usage
```
embed watermark: ./invisible-watermark -v -a encode -t bytes -m dwtDct -w 'hello' -o ./test_vectors/wm.png ./test_vectors/original.jpg
decode watermark: ./invisible-watermark -v -a decode -t bytes -m dwtDct -l 40 ./test_vectors/wm.png
positional arguments:
input The path of input
optional arguments:
-h, --help show this help message and exit
-a ACTION, --action ACTION
encode|decode (default: None)
-t TYPE, --type TYPE bytes|b16|bits|uuid|ipv4 (default: bits)
-m METHOD, --method METHOD
dwtDct|dwtDctSvd|rivaGan (default: maxDct)
-w WATERMARK, --watermark WATERMARK
embedded string (default: )
-l LENGTH, --length LENGTH
watermark bits length, required for bytes|b16|bits
watermark (default: 0)
-o OUTPUT, --output OUTPUT
The path of output (default: None)
-v, --verbose print info (default: False)
```
## Test Result
For better doc reading, we compress all images in this page, but the test is taken on 1920x1080 original image.
Methods are not robust to **resize** or aspect ratio changed **crop** but robust to **noise**, **color filter**, **brightness** and **jpg compress.**
**rivaGan outperforms the default method on crop attack.**
**only default method is ready for on-the-fly embedding.**
### Input
> * Input Image: 1960x1080 Image
> * Watermark:
> - For freq method, we use 64bits, string expression "qingquan"
> - For RivaGan method, we use 32bits, string expression "qing"
> * Parameters: only take U frame to keep image quality, ```scale=36```
### Attack Performance
**Watermarked Image**
| Attacks | Image | Freq Method | RivaGan |
| --- | --- | --- | --- |
| JPG Compress |  | Pass | Pass |
| Noise |  | Pass | Pass |
| Brightness |  | Pass | Pass |
| Overlay |  | Pass | Pass |
| Mask |  | Pass | Pass |
| crop 7x5 |  | Fail | Pass |
| Resize 50% |  | Fail | Fail |
| Rotate 30 degress |  | Fail | Fail|
### Running Speed (CPU Only)
| Image | Method | Encoding | Decoding |
| --- | --- | --- | --- |
| 1920x1080 | dwtDct | 300-350ms | 150ms-200ms |
| 1920x1080 | dwtDctSvd | 1500ms-2s | ~1s |
| 1920x1080 | rivaGan | ~5s | 4-5s |
| 600x600 | dwtDct | 70ms | 60ms |
| 600x600 | dwtDctSvd | 185ms | 320ms |
| 600x600 | rivaGan | 1s | 600ms |
### RivaGAN Experimental
Further, We will deliver the 64bit rivaGan model and test the performance on GPU environment.
Detail: [https://github.com/DAI-Lab/RivaGAN](https://github.com/DAI-Lab/RivaGAN)
Zhang, Kevin Alex and Xu, Lei and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan. Robust Invisible Video Watermarking with Attention. MIT EECS, September 2019.[[PDF](https://arxiv.org/abs/1909.01285)]