An invisible, robust copyright protection method for DNN-generated content.

Area of Science:

• Computer Science
• Artificial Intelligence
• Digital Image Processing

Background:

• Deep neural networks (DNNs) are widely used in image applications, necessitating robust copyright protection.
• Traditional visible watermarking methods often degrade image quality and introduce artifacts.
• Existing invisible watermarking techniques may lack robustness against common image distortions.

Purpose of the Study:

• To propose a novel invisible and robust copyright protection method for DNN-generated images.
• To ensure the aesthetic quality of images after copyright information embedding.
• To validate the method's effectiveness and practical feasibility in real-world scenarios.

Main Methods:

• A two-network architecture: a copyright encoder and a copyright decoder.
• The encoder embeds copyright information as invisible perturbations into images.
• A robustness module enhances the decoder's ability to extract information from distorted images.

Main Results:

• The proposed method effectively embeds and extracts copyright information.
• Encoded images maintain high aesthetic quality.
• The robustness module significantly improves resilience to various image distortions.

Conclusions:

• The novel invisible copyright protection method is effective and robust.
• The technique preserves image quality and demonstrates practical feasibility through social media testing.
• This approach offers a promising solution for copyright protection in the era of AI-generated imagery.