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.

Summary generated by The Flow from the publisher's feed. 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

5d ago

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 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 AI

Jun 11

T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking

arXiv:2606. 11698v1 Announce Type: cross Abstract: Model watermarking safeguards AI model intellectual property by embedding distinctive knowledge that induces unique behavioral signatures.

By Jian-Ping Mei, Weibin Zhang, Ao Yao, Tiantian Zhu, Jie Xiao

rag fine-tuning

arXiv AI

5d ago

Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks

arXiv:2608. 12713v1 Announce Type: cross Abstract: Watermarking LLM-generated text is an important task for tracing its provenance.

By Xiaoyan Feng, Yanjun Zhang, He Zhang, Leo Yu Zhang, Shirui Pan

llms rag benchmarks

arXiv AI

Jul 24

Making Open-Source Text LLM Watermarks Durable Against Merging

arXiv:2607. 20435v1 Announce Type: cross Abstract: Open-source LLMs (OSMs)arereaching near state-of-the-art performance, prompting prior works to trace the text they generate by embedding text watermarking algorithms directly into their weights.

By Luisa Scharff, Thibaud Gloaguen, Robin Staab, Martin Vechev

llms rag benchmarks safety

Related stories

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

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

T2S: A Rehearsal-Based Approach for Extraction-Resistant Model Watermarking

Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks

Making Open-Source Text LLM Watermarks Durable Against Merging