python library for invisible image watermark (blind image watermark)

Overview

invisible-watermark

PyPI License Python Platform Downloads

invisible-watermark is a python library and command line tool for creating invisible watermark over image.(aka. blink image watermark, digital image watermark). The algorithm doesn't reply 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

speed

  • 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 gurantee 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

  • 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: encoder/decoder model with Attention mechanism + embed watermark bits into vector.

background:

How to install

pip install invisible-watermark

Library API

Embed watermark

  • example embed 4 characters (32 bits) watermark
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
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

wm

Attacks Image Freq Method RivaGan
JPG Compress wm_jpg Pass Pass
Noise wm_noise Pass Pass
Brightness wm_darken Pass Pass
Overlay wm_overlay Pass Pass
Mask wm_mask_large Pass Pass
crop 7x5 wm_crop_7x5 Fail Pass
Resize 50% wm_resize_half Fail Fail
Rotate 30 degress wm_rotate 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

Zhang, Kevin Alex and Xu, Lei and Cuesta-Infante, Alfredo and Veeramachaneni, Kalyan. Robust Invisible Video Watermarking with Attention. MIT EECS, September 2019.[PDF]

You might also like...
[CVPR 2021] Unsupervised Degradation Representation Learning for Blind Super-Resolution
[CVPR 2021] Unsupervised Degradation Representation Learning for Blind Super-Resolution

DASR Pytorch implementation of "Unsupervised Degradation Representation Learning for Blind Super-Resolution", CVPR 2021 [arXiv] Overview Requirements

[NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior
[NeurIPS 2020] Blind Video Temporal Consistency via Deep Video Prior

pytorch-deep-video-prior (DVP) Official PyTorch implementation for NeurIPS 2020 paper: Blind Video Temporal Consistency via Deep Video Prior TensorFlo

DAN: Unfolding the Alternating Optimization for Blind Super Resolution

DAN-Basd-on-Openmmlab DAN: Unfolding the Alternating Optimization for Blind Super Resolution We reproduce DAN via mmediting based on open-sourced code

Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Real-ESRGAN Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Ported from https://github.com/xinntao/Real-ESRGAN Depend

Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)
Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)

Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)

Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)
Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021)

Towards Flexible Blind JPEG Artifacts Removal (FBCNN, ICCV 2021) Jiaxi Jiang, Kai Zhang, Radu Timofte Computer Vision Lab, ETH Zurich, Switzerland 🔥

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Implementation of temporal pooling methods studied in [ICIP'20] A Comparative Evaluation Of Temporal Pooling Methods For Blind Video Quality Assessment

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis [Paper] [Online Demo] The following results are obtained by our SCUNet with purely syn

Fast image augmentation library and easy to use wrapper around other libraries. Documentation:  https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

Comments
  • Potentially performance issues

    Potentially performance issues

    When using an image larger than 1MB the performance degradates quickly. What we observe is that with an image of 1920 × 1080 the performance is great, but using an image of 9504 × 6336 inside a container with 20GB of RAM after ~40 minutes the flask repository we put on top of the library crashes because the container is OOM. Is there a way to improve performance in this sense?

    opened by luca-simonetti 0
  • CLI decode doesn't work if output image is JPG

    CLI decode doesn't work if output image is JPG

    I'm trying to use as CLI and python script to generate a wmrked JPG but watermark decode doesn't show anything:

    C:\Users\me\AppData\Local\Programs\Python\Python310\Scripts>py invisible-watermark "F:\JPEG\_DSC5341.jpg" -v -a encode -t bytes -m dwtDct -w '1234' -o "F:\JPEG\_DSC5341-w.jpg"
    watermark length: 48
    encode time ms: 2819.3318843841553
    
    C:\Users\me\AppData\Local\Programs\Python\Python310\Scripts>py invisible-watermark "F:\JPEG\_DSC5341-w.jpg" -v -a decode -t bytes -m dwtDct -l 48
    decode time ms: 1944.9546337127686
    

    It's like there is no watermark impressed in it, unless I use a PNG as output. I posted the images I'm using for test purpouses.

    raw img test wm img test_wm

    opened by TheNemus 0
  • Example code not working

    Example code not working

    encoded the image, then decoding returned nothing, not "test" like expected.

    edit: tried with a different png image: Traceback (most recent call last): File "/home/me/whisper/decode.py", line 8, in print(watermark.decode('utf-8')) UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte

    opened by ClashSAN 0
  • How does this work?

    How does this work?

    I think a quick blurb about how the watermarks implemented by this package work would be helpful. Is it the pixel rounding that I can read about here? https://invisiblewatermark.net/how-invisible-watermarks-work.html

    opened by kevinlinxc 0
Releases(0.1.5)
Owner
Shield Mountain
Video on demand with multi DRM enterprise solutions
Shield Mountain
Kaggle DSTL Satellite Imagery Feature Detection

Kaggle DSTL Satellite Imagery Feature Detection

Konstantin Lopuhin 206 Oct 29, 2022
Users can free try their models on SIDD dataset based on this code

SIDD benchmark 1 Train python train.py If you want to train your network, just modify the yaml in the options folder. 2 Validation python validation.p

Yuzhi ZHAO 2 May 20, 2022
Mmrotate - OpenMMLab Rotated Object Detection Benchmark

OpenMMLab website HOT OpenMMLab platform TRY IT OUT 📘 Documentation | 🛠️ Insta

OpenMMLab 1.2k Jan 04, 2023
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification [pdf] The official repository for Self-Supervised Pre-Training for Transfo

Hao Luo 116 Jan 04, 2023
A pure PyTorch implementation of the loss described in "Online Segment to Segment Neural Transduction"

ssnt-loss ℹ️ This is a WIP project. the implementation is still being tested. A pure PyTorch implementation of the loss described in "Online Segment t

張致強 1 Feb 09, 2022
A collection of pre-trained StyleGAN2 models trained on different datasets at different resolution.

Awesome Pretrained StyleGAN2 A collection of pre-trained StyleGAN2 models trained on different datasets at different resolution. Note the readme is a

Justin 1.1k Dec 24, 2022
LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

LSTM Neural Network for Time Series Prediction LSTM built using the Keras Python package to predict time series steps and sequences. Includes sine wav

Jakob Aungiers 4.1k Jan 02, 2023
A data annotation pipeline to generate high-quality, large-scale speech datasets with machine pre-labeling and fully manual auditing.

About This repository provides data and code for the paper: Scalable Data Annotation Pipeline for High-Quality Large Speech Datasets Development (subm

Appen Repos 86 Dec 07, 2022
Moiré Attack (MA): A New Potential Risk of Screen Photos [NeurIPS 2021]

Moiré Attack (MA): A New Potential Risk of Screen Photos [NeurIPS 2021] This repository is the official implementation of Moiré Attack (MA): A New Pot

Dantong Niu 22 Dec 24, 2022
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

SSTNet Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks(ICCV2021) by Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui J

83 Nov 29, 2022
Prompt-BERT: Prompt makes BERT Better at Sentence Embeddings

Prompt-BERT: Prompt makes BERT Better at Sentence Embeddings Results on STS Tasks Model STS12 STS13 STS14 STS15 STS16 STSb SICK-R Avg. unsup-prompt-be

196 Jan 08, 2023
Решения, подсказки, тесты и утилиты для тренировки по алгоритмам от Яндекса.

Решения и подсказки к тренировке по алгоритмам от Яндекса Что есть внутри Решения с подсказками и комментариями; рекомендую сначала смотреть md файл п

Yankovsky Andrey 50 Dec 26, 2022
[ICCV 2021] Relaxed Transformer Decoders for Direct Action Proposal Generation

RTD-Net (ICCV 2021) This repo holds the codes of paper: "Relaxed Transformer Decoders for Direct Action Proposal Generation", accepted in ICCV 2021. N

Multimedia Computing Group, Nanjing University 80 Nov 30, 2022
NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

NEATEST: Evolving Neural Networks Through Augmenting Topologies with Evolution Strategy Training

Göktuğ Karakaşlı 16 Dec 05, 2022
This is an official implementation for "PlaneRecNet".

PlaneRecNet This is an official implementation for PlaneRecNet: A multi-task convolutional neural network provides instance segmentation for piece-wis

yaxu 50 Nov 17, 2022
2020 CCF大数据与计算智能大赛-非结构化商业文本信息中隐私信息识别-第7名方案

2020CCF-NER 2020 CCF大数据与计算智能大赛-非结构化商业文本信息中隐私信息识别-第7名方案 bert base + flat + crf + fgm + swa + pu learning策略 + clue数据集 = test1单模0.906 词向量

67 Oct 19, 2022
Angular & Electron desktop UI framework. Angular components for native looking and behaving macOS desktop UI (Electron/Web)

Angular Desktop UI This is a collection for native desktop like user interface components in Angular, especially useful for Electron apps. It starts w

Marc J. Schmidt 49 Dec 22, 2022
The pyrelational package offers a flexible workflow to enable active learning with as little change to the models and datasets as possible

pyrelational is a python active learning library developed by Relation Therapeutics for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximat

Relation Therapeutics 95 Dec 27, 2022
An implementation of the proximal policy optimization algorithm

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 09, 2022
Repository for Driving Style Recognition algorithms for Autonomous Vehicles

Driving Style Recognition Using Interval Type-2 Fuzzy Inference System and Multiple Experts Decision Making Created by Iago Pachêco Gomes at USP - ICM

Iago Gomes 9 Nov 28, 2022