Watermark Removal

Watermark removal research focuses on developing techniques to eliminate embedded digital watermarks from images, audio, or even AI models, often to circumvent copyright protection or ownership verification. Current efforts concentrate on self-supervised and adversarial learning methods, employing convolutional neural networks (CNNs), generative adversarial networks (GANs), diffusion models, and transformers to achieve this, with a particular emphasis on improving robustness against various watermarking schemes. The success of these methods has significant implications for intellectual property protection, particularly in the rapidly expanding fields of AI-generated content and digital media, highlighting the ongoing arms race between watermarking and removal techniques. The development of more robust watermarking and more effective removal techniques is crucial for balancing copyright protection with the legitimate use of digital content.