Watermarking Framework

Watermarking frameworks aim to embed imperceptible signals into digital content generated by AI models, enabling the identification of machine-generated content and deterring misuse. Current research focuses on developing robust watermarking techniques for various modalities, including text and images, often employing error-correcting codes, distribution-preserving methods, and neural network architectures like invertible neural networks to enhance resilience against removal attacks. This field is crucial for addressing concerns around copyright infringement, the spread of misinformation, and the responsible deployment of increasingly sophisticated AI technologies.