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A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers
Published on: January 18, 2020
7.8K
Deep Image Watermarking to JPEG Compression Based on Mixed-Frequency Channel Attention.
Jun Tan1, Yinan Hu1, Ziming Shi2
1The Key Laboratory of Advanced Design and Intelligent Computing, School of Software Engineering, Dalian University, Dalian 116622, China.
Computational and Mathematical Methods in Medicine
|July 25, 2022
Summary
This study introduces a novel deep blind watermarking method that enhances robustness against JPEG compression and other distortions. The approach improves visual quality and copyright protection using mixed-frequency channel attention.
Area of Science:
- Computer Science
- Image Processing
- Digital Watermarking
Background:
- Deep blind watermarking is crucial for copyright protection.
- Existing methods struggle to balance robustness against JPEG compression with visual quality.
- Full utilization of image channel features remains a challenge.
Purpose of the Study:
- To propose a novel deep blind watermarking algorithm.
- To enhance robustness against JPEG compression and common distortions.
- To achieve high visual quality in watermarked images.
Main Methods:
- Developed a mixed-frequency channel attention mechanism within an encoder-decoder architecture.
- Utilized 2D-Discrete Cosine Transform (2D-DCT) domain frequency components for channel feature weighting.
- Implemented frequency analysis in the channel dimension to suppress irrelevant features and enhance watermarking embedding.
Main Results:
- Achieved a Peak Signal-to-Noise Ratio (PSNR) over 38.
- Maintained a Bit Error Rate (BER) below 0.01% under JPEG compression (Quality Factor Q=50).
- Demonstrated excellent robustness against Gaussian filter, cropping, and dropout attacks.
Conclusions:
- The proposed mixed-frequency channel attention method significantly improves robustness against JPEG compression and other distortions.
- The algorithm effectively balances high visual quality with strong copyright protection.
- This approach advances deep blind watermarking techniques by leveraging frequency-specific channel features.
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