arXiv AI August 12, 2026 By Ziwei Zhang, Juan Wen, Wanli Peng, Zhengxian Wu, Yinghan Zhou, Yiming Xue

Protecting Creative Writing Copyright against AI Imitation via Implicit Watermarking

arXiv:2504. 00035v4 Announce Type: replace-cross Abstract: Large language models (LLMs) enable powerful knowledge injection through approaches such as in-context learning and fine-tuning, but they also introduce new risks of unauthorized imitation of high-value creative works.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 10

Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization

arXiv:2606. 09909v1 Announce Type: cross Abstract: With the growing concerns over copyright infringement in diffusion-based customization, adversarial attacks have emerged as a prominent defense strategy to prevent malicious content forgery in personalized image generation.

By Ziang Xu, Wenbo Yu, Hongyao Yu, Hao Fang, Jiawei Kong, Bin Chen, Hao Wu, Shu-Tao Xia, Zhiyong Wu

diffusion benchmarks safety

arXiv AI

Aug 14

Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models

arXiv:2512. 13325v2 Announce Type: replace-cross Abstract: Securing digital text is becoming increasingly relevant due to the widespread use of large language models.

By Malte Hellmeier

llms rag

arXiv Machine Learning

Sep 23

The Challenge of Identifying the Origin of Black-Box Large Language Models

arXiv:2503.04332v2 Announce Type: replace-cross Abstract: The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we...

By Ziqing Yang, Yixin Wu, Yun Shen, Wei Dai, Michael Backes, Yang Zhang

llms rag benchmarks safety

arXiv Computer Vision

3d ago

Persistent Watermarking of Text-to-Image Models

arXiv:2609.39024v1 Announce Type: new Abstract: Text-to-image (T2I) generation is gaining increasing popularity with the general public, motivating the development of reliable mechanisms for copyrigh...

By Dixi Yao, Kaiwen Chen, Tahseen Rabbani, Tian Li

diffusion fine-tuning

arXiv AI

Jun 10

Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion

arXiv:2606. 10099v1 Announce Type: cross Abstract: The rapid development of large language models (LLMs) has raised concerns about misuse such as plagiarism, misinformation, and automated influence operations, motivating the need for robust detectors.

By Rafael Rivera Soto, Barry Chen, Nicholas Andrews

llms benchmarks safety

arXiv Machine Learning

4d ago

Dataset Watermarking with Provable Black-Box Detection

The paper introduces a dataset watermarking technique that embeds a watermark by increasing the co‑occurrence of randomly selected word pairs through meaning‑preserving local edits. The watermark can be detected solely from generated text with provable false‑positive control, and experiments on four base models and three datasets show reliable detection (p < 0.01) even when the watermarked data constitutes less than 5% of fine‑tuning tokens. Compared to existing methods, the approach better preserves benchmark utility and semantic integrity.

By Pengrun Huang, Kamalika Chaudhuri, Yu-Xiang Wang

llms fine-tuning benchmarks

One email a morning, machine-written

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Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization

Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models

The Challenge of Identifying the Origin of Black-Box Large Language Models

Persistent Watermarking of Text-to-Image Models

Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion

Dataset Watermarking with Provable Black-Box Detection