| Challenge: | Generative models have produced high-quality content, but they pose security risks . a new framework for deep learning systems enables the tracing of AI-generated content back to its source . |
| Approach: | They propose a digital forensics framework that embeds a secret watermark into the generated output and a "depth watermark" this watermark strengthens the link between content and generator, enabling accurate tracing while maintaining the quality of the generated content. |
| Outcome: | The proposed framework ensures accurate tracing while maintaining quality of generated content. |
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A Survey on Detection of LLMs-Generated Content (2024.findings-emnlp)
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Hanxi Guo, Siyuan Cheng, Xiaolong Jin, Zhuo Zhang, Guangyu Shen, Kaiyuan Zhang, Shengwei An, Guanhong Tao, Xiangyu Zhang
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On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
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