[arXiv 2025-04-02] 저자: Lele Cao | 초록: The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio
[arXiv 2026-05-09] 저자: Zhenxin Ai, Haiyun He | 초록: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddin
[학술논문 2024] 저자: Weixin Liang, Zachary Izzo, Yaohui Zhang | 인용수: 68 | 초록: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took pl
[학술논문 2024] 저자: Haowei Hua, Co-Jiayu Yao | 인용수: 20 | 초록: Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K-12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completin
[학술논문 2023] 저자: Junchao Wu, Shu Yang, Runzhe Zhan | 인용수: 30 | 초록: The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artisti
[arXiv 2025-04-02] 저자: Lele Cao | 초록: The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio
[arXiv 2026-05-09] 저자: Zhenxin Ai, Haiyun He | 초록: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddin
[학술논문 2024] 저자: Weixin Liang, Zachary Izzo, Yaohui Zhang | 인용수: 68 | 초록: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took pl
[학술논문 2024] 저자: Haowei Hua, Co-Jiayu Yao | 인용수: 20 | 초록: Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K-12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completin
[학술논문 2023] 저자: Junchao Wu, Shu Yang, Runzhe Zhan | 인용수: 30 | 초록: The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artisti
[arXiv 2025-04-02] 저자: Lele Cao | 초록: The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio
[arXiv 2026-05-09] 저자: Zhenxin Ai, Haiyun He | 초록: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddin
[학술논문 2024] 저자: Weixin Liang, Zachary Izzo, Yaohui Zhang | 인용수: 68 | 초록: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took pl
[학술논문 2024] 저자: Haowei Hua, Co-Jiayu Yao | 인용수: 20 | 초록: Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K-12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completin
[학술논문 2023] 저자: Junchao Wu, Shu Yang, Runzhe Zhan | 인용수: 30 | 초록: The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artisti
[arXiv 2025-04-02] 저자: Lele Cao | 초록: The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio
[arXiv 2026-05-09] 저자: Zhenxin Ai, Haiyun He | 초록: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddin
[학술논문 2024] 저자: Weixin Liang, Zachary Izzo, Yaohui Zhang | 인용수: 68 | 초록: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took pl
[학술논문 2024] 저자: Haowei Hua, Co-Jiayu Yao | 인용수: 20 | 초록: Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K-12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completin
[학술논문 2023] 저자: Junchao Wu, Shu Yang, Runzhe Zhan | 인용수: 30 | 초록: The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artisti
[arXiv 2025-04-02] 저자: Lele Cao | 초록: The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio
[arXiv 2026-05-09] 저자: Zhenxin Ai, Haiyun He | 초록: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddin
[학술논문 2024] 저자: Weixin Liang, Zachary Izzo, Yaohui Zhang | 인용수: 68 | 초록: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took pl
[학술논문 2024] 저자: Haowei Hua, Co-Jiayu Yao | 인용수: 20 | 초록: Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K-12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completin
[학술논문 2023] 저자: Junchao Wu, Shu Yang, Runzhe Zhan | 인용수: 30 | 초록: The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artisti
[arXiv 2025-04-02] 저자: Lele Cao | 초록: The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio
[arXiv 2026-05-09] 저자: Zhenxin Ai, Haiyun He | 초록: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddin
[학술논문 2024] 저자: Weixin Liang, Zachary Izzo, Yaohui Zhang | 인용수: 68 | 초록: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took pl
[학술논문 2024] 저자: Haowei Hua, Co-Jiayu Yao | 인용수: 20 | 초록: Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K-12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completin
[학술논문 2023] 저자: Junchao Wu, Shu Yang, Runzhe Zhan | 인용수: 30 | 초록: The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artisti
[arXiv 2025-04-02] 저자: Lele Cao | 초록: The rapid advancement of generative artificial intelligence (GenAI) has revolutionized content creation across text, visual, and audio domains, simultaneously introducing significant risks such as misinformation, identity fraud, and content manipulation. This paper presents a practical survey of watermarking techniques designed to proactively detect GenAI content. We develop a structured taxonomy categorizing watermarking methods for text, visual, and audio
[arXiv 2026-05-09] 저자: Zhenxin Ai, Haiyun He | 초록: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddin
[학술논문 2024] 저자: Weixin Liang, Zachary Izzo, Yaohui Zhang | 인용수: 68 | 초록: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took pl
[학술논문 2024] 저자: Haowei Hua, Co-Jiayu Yao | 인용수: 20 | 초록: Generative AI models, including ChatGPT, Gemini, and Claude, are increasingly significant in enhancing K-12 education, offering support across various disciplines. These models provide sample answers for humanities prompts, solve mathematical equations, and brainstorm novel ideas. Despite their educational value, ethical concerns have emerged regarding their potential to mislead students into copying answers directly from AI when completin
[학술논문 2023] 저자: Junchao Wu, Shu Yang, Runzhe Zhan | 인용수: 30 | 초록: The powerful ability to understand, follow, and generate complex language emerging from large language models (LLMs) makes LLM-generated text flood many areas of our daily lives at an incredible speed and is widely accepted by humans. As LLMs continue to expand, there is an imperative need to develop detectors that can detect LLM-generated text. This is crucial to mitigate potential misuse of LLMs and safeguard realms like artisti
[arXiv 2026-05-09] 저자: Zhenxin Ai, Haiyun He | 초록: Watermarking for large language models (LLMs) is a promising approach for detecting LLM-generated text and enabling responsible deployment. However, existing watermarking methods are often vulnerable to semantic-invariant attacks, such as paraphrasing. We propose PASA, a principled, robust, and distortion-free watermarking algorithm that embeds and detects a watermark at the semantic level. PASA operates on semantic clusters in a latent embeddin
[학술논문 2026] 저자: Hulya Kocagul, Melik Kartal | 인용수: 1 | 초록: AI systems are entering criminal proceedings as evidence producers, risk assessors, and decision shapers, yet the procedural architecture of adversarial and mixed systems was built on the assumption that evidence originates from human actors whose reasoning can be reconstructed and challenged. This article introduces the concept of evidentiary authority—the power to determine what counts as reliable evidence and how much weight it carrie
[arXiv 2025-11-27] 저자: Avinash Agarwal, Peeyush Agarwal, Manisha J. Nene | 초록: As Artificial Intelligence becomes increasingly embedded in critical telecommunications infrastructure, existing legal frameworks remain ill-equipped to address the distinct risks this development introduces. This paper proposes the AI Regulatory Readiness Index (ARRI), a reproducible instrument for doctrinally assessing the legal preparedness of national frameworks to govern AI in critical digital infrastructure, and