Watermarking Language Models through Language Models

This paper presents a novel framework for watermarking language models through prompts generated by language models. The proposed approach utilizes a multi-model setup, incorporating a Prompting language model to generate watermarking instructions, a Marking language model to embed watermarks within generated content, and a Detecting language model to verify the presence of these watermarks. Experiments are conducted using ChatGPT and Mistral as the Prompting and Marking language models, with detection accuracy evaluated using a pretrained classifier model. Results demonstrate that the proposed framework achieves high classification accuracy across various configurations, with 95% accuracy for ChatGPT, 88.79% for Mistral. These findings validate the and adaptability of the proposed watermarking strategy across different language model architectures. Hence the proposed framework holds promise for applications in content attribution, copyright protection, and model authentication.

Deep Learning based Image Watermarking: A Brief Survey

The act of secretly embedding and extracting a watermark on a cover image to protect it is known as image watermarking. In recent years, deep learning-based image watermarking techniques have been emerging one after another. To study the state-of-the-art, this survey categorizes cutting-edge deep learning-based image watermarking techniques into Embedder-Extractor Joint Training, Deep Networks as a Feature Transformation, and Hybrid schemes. Research directions in each category are also analyzed and summarized. Additionally, potential future research directions are discussed to envision future studies.