A Robust Semantic Watermark for Detecting Machine-Generated Text

Основні поняття

A novel semantic watermarking algorithm, SEMSTAMP, that is robust to paraphrase attacks and preserves the quality of generated text.

Анотація

The paper introduces SEMSTAMP, a semantic watermarking algorithm for detecting machine-generated text. The key ideas are:

1. Watermark at the sentence-level semantic space instead of the token-level vocabulary to be robust against paraphrasing.
2. Use a paraphrase-robust sentence encoder trained with contrastive learning to map sentences into the semantic embedding space.
3. Partition the semantic space using locality-sensitive hashing (LSH) and conduct rejection sampling to generate sentences that fall within the "valid" regions.
4. Add a margin-based constraint to further enhance the robustness of the LSH signatures against paraphrasing.

The authors also propose a novel "bigram paraphrase" attack that effectively weakens token-level watermarking algorithms while only causing minor degradation to SEMSTAMP. Experiments show that SEMSTAMP outperforms a token-level watermarking baseline in detection accuracy under various paraphrase attacks, while also preserving the quality of generated text.

Статистика

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Цитати

"We propose SEMSTAMP, a robust sentence-level semantic watermarking algorithm that uses locality-sensitive hashing (LSH) to partition the semantic space of sentences."
"To stress-test the robustness of watermarking algorithms, we develop a novel attack method that minimizes bigram overlap during paraphrasing, and name it the bigram paraphrase attack."
"Experimental results demonstrate that our proposed semantic watermark remains effective while token-level watermarks suffer significantly from the bigram attack."

Ключові висновки, отримані з

SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

by Abe Bohan Ho... о arxiv.org 04-23-2024

https://arxiv.org/pdf/2310.03991.pdf

Глибші Запити

How can the generation speed of SEMSTAMP be further improved without compromising its robustness?

To improve the generation speed of SEMSTAMP without compromising its robustness, one approach could be to implement batched sampling of candidate next sentences. By conducting batched sampling, multiple candidate sentences can be generated simultaneously across multiple GPUs, allowing for parallel decoding and speeding up the generation process. This method would help reduce the time taken for rejection sampling while maintaining the integrity of the watermarking process. Additionally, optimizing the rejection margin parameter and fine-tuning the algorithm for efficient batch processing can further enhance the speed of SEMSTAMP generation.

What other types of attacks beyond paraphrasing could potentially weaken the SEMSTAMP watermark, and how can the algorithm be made more resilient?

Apart from paraphrasing attacks, SEMSTAMP may be vulnerable to inter-sentence level attacks, where the relationships between sentences are manipulated to disrupt the watermark. To enhance the resilience of SEMSTAMP against such attacks, incorporating inter-sentence constraints or dependencies during watermark injection and detection could be beneficial. By considering the context and coherence between sentences, the algorithm can better detect anomalies or manipulations that aim to disrupt the watermark. Additionally, exploring advanced encryption techniques or incorporating dynamic watermarking strategies that adapt to different attack scenarios can further strengthen the algorithm's resilience against various types of attacks.

How can the SEMSTAMP approach be extended to other modalities beyond text, such as images or audio, to enable robust watermarking for multimodal content generation?

To extend the SEMSTAMP approach to other modalities like images or audio for robust watermarking in multimodal content generation, the algorithm can be adapted to encode and partition the semantic space of the respective modalities. For images, a similar semantic embedding model can be used to map image features into a high-dimensional space, followed by locality-sensitive hashing (LSH) for partitioning the semantic space. The watermarking process would involve injecting watermarks into the image embeddings and applying rejection sampling based on the LSH partitions. Similarly, for audio data, the algorithm can utilize audio embeddings and LSH techniques to watermark and detect machine-generated audio content. By customizing the SEMSTAMP approach for different modalities, it can provide a comprehensive solution for robust watermarking in multimodal content generation, ensuring the authenticity and integrity of generated content across various formats.