Duwak: Dual Watermarks in Large Language Models

Khái niệm cốt lõi

Efficiently embedding dual watermarks in large language models to enhance detection efficiency and text quality.

Tóm tắt

This article introduces Duwak, a dual watermarking approach for large language models. It focuses on improving watermark detection efficiency and text quality by embedding two independent secret patterns in token probability distribution and sampling schemes. The article discusses the theoretical framework, evaluation results, related studies, limitations, and impact statements of Duwak.

Abstract:

• Discusses the importance of auditing large language models (LLMs) and proposes Duwak for efficient watermarking.
• Introduces the concept of embedding dual secret patterns in token probability distribution and sampling schemes.
• Evaluates Duwak's performance against existing watermark techniques under various post-editing attacks.

Introduction:

• Highlights the risks associated with misusing LLMs for generating incorrect content.
• Emphasizes the need for watermarking LLM content to govern applications and prevent misuse.

Data Extraction:

• Our anonymous code is available at https://anonymous.4open.science/r/Duwak-BDE5.

Quotations:

• "Existing watermark techniques are shown effective in embedding single human-imperceptible and machine-detectable patterns without significantly affecting generated text quality." - Author

Thống kê

Our anonymous code is available at https://anonymous.4open.science/r/Duwak-BDE5.

Trích dẫn

"Existing watermark techniques are shown effective in embedding single human-imperceptible and machine-detectable patterns without significantly affecting generated text quality." - Author

Thông tin chi tiết chính được chắt lọc từ

Duwak

by Chaoyi Zhu,J... lúc arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13000.pdf

Yêu cầu sâu hơn

How can the concept of dual watermarks be applied to other types of models or technologies?

The concept of dual watermarks, as demonstrated in the Duwak model for large language models, can be applied to various other types of models and technologies. For instance:

Image Processing: In image processing applications, dual watermarks could be embedded in images to ensure traceability and authenticity. One watermark could focus on altering pixel values while another could modify metadata.
Audio Recognition: Dual watermarks in audio recognition systems could involve embedding one watermark in the frequency domain and another in the time domain, enhancing robustness against signal processing attacks.
Video Streaming: For video streaming platforms, dual watermarks could be used to embed copyright information within videos at different levels (e.g., frame level and segment level) for enhanced protection.

What are the potential drawbacks or limitations of using dual watermarks in large language models?

While dual watermarks offer several advantages, they also come with certain drawbacks and limitations:

Complexity: Implementing two separate watermarking schemes within a model increases complexity and may require more computational resources.
Interference: The interaction between two distinct watermark signals may lead to interference issues that affect detection efficiency or text quality.
Detection Challenges: Detecting multiple watermarks simultaneously might pose challenges due to overlapping signals or conflicting requirements.

How might advancements in watermarking techniques impact the future development of AI technologies?

Advancements in watermarking techniques have significant implications for AI technologies:

Enhanced Security: Improved watermarking methods can bolster security measures by enabling better tracking and verification mechanisms for AI-generated content.
Regulatory Compliance: Advanced watermarking solutions help meet regulatory requirements related to content ownership rights, data integrity, and accountability.
Trustworthiness: By ensuring the authenticity and provenance of AI outputs through robust watermarking techniques, trust among users is strengthened.
Innovation Acceleration: Reliable watermarking methods encourage innovation by fostering a secure environment for sharing intellectual property without fear of unauthorized use.

These advancements pave the way for more responsible deployment of AI technologies across various industries while addressing concerns related to data privacy, intellectual property rights, and ethical considerations surrounding machine-generated content.