Datasets:

fineset-io
/

llm-watermarking-papers

id string	sources list	title string	abstract string	authors list	categories list	fields_of_study list	published_date timestamp[s]	url string	pdf_url string	arxiv_id string	doi string	citation_count int64	influential_citation_count int64	has_code bool	code_url string	venue string	quality_score float64
d3c5d51b8b48fee8884935e35ad1b30815f0de2328721b99650fd70f867fb623	[ "arxiv" ]	Signature filtering: a lightweight enhancement for statistical watermark detection in large language models	Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals are weak, texts are repetitive, or watermarks are edited. We propose signature filtering, a detection-time module that enhances watermark detection without modifying water...	[ "Chih-Duo Hong", "Yen-Pang Chen", "Fang Yu" ]	[ "cs.LG", "cs.CR" ]	[]	2026-06-16T00:00:00	https://arxiv.org/abs/2606.18430	https://arxiv.org/pdf/2606.18430v1	2606.18430	null	0	0	false	null	null	0.35
36bbccb7bd56d3f0718119cbbba0a975d8dff50311b11d120810105361d91875	[ "arxiv", "semantic_scholar" ]	DuraMark: Duration-Embedded Watermarking in LLM-based TTS	Large language model (LLM)-based text-to-speech (TTS) models have achieved remarkable voice cloning capabilities, raising concerns about potential deepfake misuse. Speech watermarking mitigates this by embedding traceable information into generated speech. Mainstream watermarking methods operate at the signal level (wa...	[ "Zhenwei Mou", "Weili Jiang", "Liping Chen", "Zhen-Hua Ling", "Kong Aik Lee", "Kai Gao", "Boyu Zhao" ]	[ "eess.AS", "cs.SD" ]	[ "Engineering", "Computer Science" ]	2026-06-13T00:00:00	https://arxiv.org/abs/2606.15264	https://arxiv.org/pdf/2606.15264v1	2606.15264	null	0	0	false	null	null	0.35
645792ac545af27e59db601478c901e8e9896e0a6239c4189dbf7e6e4d840a29	[ "arxiv", "semantic_scholar" ]	VoxWatermark: A Large-Scale Benchmark for Audio Watermark Detection under Perturbations	With the rapid deployment of speech generation systems in open environments, providing verifiable source attribution and copyright accountability for audio content has become critical. A gap in current research is the lack of a unified benchmark that systematically compares different watermark injection methods under r...	[ "Farnaz Sedaghati", "Yuxi Wang", "Zicheng Weng", "Wei Rao" ]	[ "eess.AS", "cs.SD" ]	[ "Engineering", "Computer Science" ]	2026-06-13T00:00:00	https://arxiv.org/abs/2606.15187	https://arxiv.org/pdf/2606.15187v1	2606.15187	null	0	0	false	null	null	0.35
3d6a724948b60a0b25df7bec29290f8bef58491e0dd99adff921859434ee4959	[ "arxiv", "semantic_scholar" ]	Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching	Text-attributed graphs (TAGs) underlie real-world applications such as citation networks, social media, and e-commerce. Few-shot graph learning on TAGs is hard: with only a handful of labels per class and the rest of the graph unannotated, neither GNNs nor LLMs can learn well on their own. GNNs read topology and fail o...	[ "Zhuoyi Peng", "Hanlin Gu", "Lixin Fan", "Yi Yang" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-06-10T00:00:00	https://arxiv.org/abs/2606.11583	https://arxiv.org/pdf/2606.11583v1	2606.11583	null	0	0	true	https://github.com/llmgnncoteaching/LLM-GNN-Coteaching	null	0.65
5ea3182923a447700ff7de9a75dc9d912a92dbc16a5d4fdefccb458d42d9e167	[ "arxiv", "semantic_scholar" ]	YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition	Large language models (LLMs) drive significant financial innovations, yet their high-concurrency deployment is severely bottlenecked by KV cache memory overhead, which inflates infrastructure costs and throttles scalability. To address this, we propose YouZhi-LLM, a highly efficient financial LLM empowered by a compreh...	[ " PSBC LLM Team", " Huawei LLM Team", "Ruihan Long", "Junjie Wu", "Tianan Zhang", "Duo Zhang", "Yaozong Wu", "Jinbin Fu", "Chang Liu", "Zhentao Tang", "Wenshuang Yang", "Xin Wang", "Zhihao Song", "Ning Huang", "Wenjing Xu", "Shuai Zong", "Shupei Sun", "Sen Wang", "Jing Hu", "Bi...	[ "cs.CL" ]	[ "Computer Science" ]	2026-06-04T00:00:00	https://arxiv.org/abs/2606.05868	https://arxiv.org/pdf/2606.05868v1	2606.05868	null	0	0	false	null	null	0.35
8f35dfee090d7e070c537422efd8b206fa120c84eadab22b58561a059e343396	[ "arxiv", "semantic_scholar" ]	De-attribute to Forget for LLM Unlearning	The rapid development of large language models (LLMs) has raised concerns on the use of inappropriate data for training, which has led to a growing interest in LLM unlearning. Many existing LLM unlearning approaches rely on optimizing prediction loss(es), such as maximizing the loss on the forget set, but often face cr...	[ "Xinyang Lu", "Jiabao Pan", "Rachael Hwee Ling Sim", "See-Kiong Ng", "Anthony Kum Hoe Tung", "Bryan Kian Hsiang Low" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2026-05-29T00:00:00	https://arxiv.org/abs/2605.30919	https://arxiv.org/pdf/2605.30919v1	2605.30919	null	0	0	false	null	null	0.35
0542525990a888bafd1736e1de159572af2ee880c8d9ac763a5890dbb2f6b15d	[ "arxiv", "semantic_scholar" ]	Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking across Datasets, Models, and Generated Content	This paper presents a survey and taxonomy of LLM fingerprinting and watermarking for identity, ownership verification, provenance, and generated-content attribution. Large language models (LLMs) require substantial investments in data, computation, and expertise, and are increasingly deployed in high-stakes settings, m...	[ "Bing Liu", "Shunping Wang", "Yufan Zhu", "Xinyi Yu", "Jing Huang", "Linkang Du", "Hongbin Pei", "Wei Luo" ]	[ "cs.CR", "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-05-28T00:00:00	https://arxiv.org/abs/2605.29245	https://arxiv.org/pdf/2605.29245v1	2605.29245	null	0	0	false	null	null	0.35
7586690ab6d13400d3c96e62dae97faece01ef09572036f0d5459cc55927c382	[ "arxiv", "semantic_scholar" ]	Linear Ensembles Wash Away Watermarks: On the Fragility of Distributional Perturbations in LLMs	Watermarking embeds statistical signatures in AI-generated text for detection and attribution. We reveal a fundamental vulnerability: when users access multiple models (today's reality), watermarks trivially fail. Watermarks perturb output distributions away from the original, and in competitive markets, these perturba...	[ "Zhihao Wu", "Gracia Gong", "Qinglin Zhu", "Yudong Chen", "Runcong Zhao" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-05-28T00:00:00	https://arxiv.org/abs/2605.30501	https://arxiv.org/pdf/2605.30501v1	2605.30501	null	0	0	false	null	null	0.35
b3cb9bc4db16463797c29f7aeb159da90a71881165b7cf0408eb0ab78e576a67	[ "arxiv", "semantic_scholar" ]	LoRA-Key: User-Centric LoRA Watermarking for Text-to-Image Diffusion Models	Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing text-to-image diffusion models, enabling lightweight modules that are shared, reused, and commercialized as independent assets. This LoRA-centric ecosystem shifts copyright protection from foundation models to distributed LoRA modules, which ...	[ "Yaopeng Wang", "Qingliang Wang", "Zhibo Wang", "Huiyu Xu", "Jiacheng Du", "Qiu Wang", "Jia-Li Yin", "Kui Ren" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-05-28T00:00:00	https://arxiv.org/abs/2605.29569	https://arxiv.org/pdf/2605.29569v2	2605.29569	null	0	0	false	null	null	0.35
c58fda2c4495fb1621bac30e1db91a249b9baa20d5483aef007d69b6aa97e5b2	[ "arxiv", "semantic_scholar" ]	AliMark: Enhancing Robustness of Sentence-Level Watermarking Against Text Paraphrasing	Existing sentence-level watermarking methods enhance robustness to paraphrasing by anchoring watermarks in sentence semantics. However, their prefix-based designs remain vulnerable to structural perturbations, such as sentence splitting and merging, which commonly arise under strong paraphrasers like DIPPER and GPT-3.5...	[ "Yuexin Li", "Wenjie Qu", "Linyu Wu", "Yulin Chen", "Yufei He", "Tri Cao", "Bryan Hooi", "Jiaheng Zhang" ]	[ "cs.CR", "cs.AI", "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2026-05-28T00:00:00	https://arxiv.org/abs/2605.29434	https://arxiv.org/pdf/2605.29434v1	2605.29434	null	0	0	false	null	null	0.35
6a5a7ec08204463aceb9613ae0c7994360ccafca7964026d5e869552678abba8	[ "arxiv", "semantic_scholar" ]	Blind PRNG Hijacking: An Undetectable Integrity-Preserving Attack Against LLM Watermarking	Cryptographic watermarking is a leading defense for attributing text generated by large language models (LLMs). Existing schemes, including KGW, Unigram, and DipMark, derive their security guarantees from the assumption that the underlying pseudo-random number generator (PRNG) is trustworthy. This work introduces SeedH...	[ "Ziyang You", "Huilong He", "Xiaoke Yang", "Xuxing Lu" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-05-27T00:00:00	https://arxiv.org/abs/2605.28632	https://arxiv.org/pdf/2605.28632v1	2605.28632	null	0	0	true	null	null	0.65
c2468be3387dbc2b27b8dc24890db39aed51a186a172b3ad7d121bb31b874a37	[ "arxiv", "semantic_scholar" ]	SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness	Semantic-level watermarking (SWM) improves robustness against text modifications by treating sentences as the basic unit. However, robustness to paragraph-level paraphrasing remains difficult because such attacks globally disrupt watermark signals by changing sentence order. In this work, we propose SAMark, a self-anch...	[ "Jiahao Huo", "Wenjie Qu", "Yibo Yan", "Kening Zheng", "Jiaheng Zhang", "Xuming Hu", "Philip S. Yu", "Mingxun Zhou" ]	[ "cs.CR", "cs.AI", "cs.CL" ]	[ "Computer Science" ]	2026-05-25T00:00:00	https://arxiv.org/abs/2605.25796	https://arxiv.org/pdf/2605.25796v2	2605.25796	null	0	0	false	null	null	0.35
7f7248dc441e54fd20a8c9eef4bd0fc46259718c866e3e0c1684dda7500d743a	[ "arxiv", "semantic_scholar" ]	MemMark: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systems	Memory-backed agents need provenance that can survive leaked or migrated snapshots, where logs, visible outputs, and trusted metadata may be absent. We propose MemMark, a state-evolution attribution watermark that embeds an owner-controlled signal into latent memory-write decisions. At each internal LLM call, MemMark s...	[ "Haobo Zhang", "Xutao Mao", "Guangyuan Dong", "Ziwei Li", "Xuanbo Su", "Kaijie Chen", "Jing Yang", "Zheng Lin" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-05-24T00:00:00	https://arxiv.org/abs/2605.25002	https://arxiv.org/pdf/2605.25002v2	2605.25002	null	1	0	false	null	null	0.35
620da0a1a37d61bd9d7cf55c8cbafb9bfcf3c077b8511e52281d8d7081be0276	[ "arxiv", "semantic_scholar" ]	Robust LLM Watermarking with Minimal Semantic Distortion for IP Protection	Proprietary large language models (LLMs) face risks of intellectual property (IP) violation, as adversaries can replicate an LLM by collecting input-output pairs to train a surrogate model, causing financial setbacks. Watermarks offer a promising defense to verify ownership, but existing methods often struggle with sem...	[ "Kieu Dang", "Phung Lai", "NhatHai Phan", "Yelong Shen", "Ruoming Jin" ]	[ "cs.CR", "cs.CL" ]	[ "Computer Science" ]	2026-05-22T00:00:00	https://arxiv.org/abs/2605.23175	https://arxiv.org/pdf/2605.23175v1	2605.23175	null	0	0	false	null	null	0.35
035bd5b53cf9c14c1070fe3981480d44a32aefed80aefe529e166f6e25ba4fe4	[ "arxiv", "semantic_scholar" ]	Watermarks Attack Watermarks: Re-Watermarking as a Generic Removal Strategy	Watermarking combines an imperceptible change to an input image that will trigger a detector, to assert provenance and protect intellectual property. The literature has shown great interest in attacks on watermarking schemes: attackers are clearly motivated to steal copyrighted material or circumvent legislated deepfak...	[ "Maria Bulychev", "Neil G. Marchant", "Benjamin I. P. Rubinstein" ]	[ "cs.CR", "cs.CV" ]	[ "Computer Science" ]	2026-05-16T00:00:00	https://arxiv.org/abs/2605.16796	https://arxiv.org/pdf/2605.16796v1	2605.16796	null	0	0	false	null	null	0.35
729297aaa8fbcee02bd33cad2b5c1b59666e8ccfb4d98a0db2fc4fa762791f9b	[ "arxiv", "semantic_scholar" ]	Dynamics-Level Watermarking of Flow Matching Models with Random Codes	We introduce a dynamics-level approach to watermarking generative models. Rather than embedding signals into model weights or outputs, we embed the watermark directly into the learned continuous dynamics -- the velocity field of a flow matching model. We formulate this as random coding over a continuous channel: a key-...	[ "Shuchan Wang" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-05-15T00:00:00	https://arxiv.org/abs/2605.16239	https://arxiv.org/pdf/2605.16239v1	2605.16239	null	0	0	true	https://github.com/ShuchanWang/flow-matching-dynamics-watermarking	null	0.65
9ecdb2d46c66f5eadba81b78f7d3a933611c9813207216b9c9c8d4514bec98b8	[ "arxiv", "semantic_scholar" ]	DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection	The effective detection and governance of Large Language Model (LLM) generated content has become increasingly critical due to the growing risk of misuse. Despite the impressive performance of existing detectors, their reliability and potential in multilingual, real-world scenarios remain largely underexplored. In this...	[ "Junchao Wu", "Yefeng Liu", "Chenyu Zhu", "Hao Zhang", "Zeyu Wu", "Tianqi Shi", "Yichao Du", "Longyue Wang", "Weihua Luo", "Jinsong Su", "Derek F. Wong" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-05-15T00:00:00	https://arxiv.org/abs/2605.15518	https://arxiv.org/pdf/2605.15518v2	2605.15518	null	0	0	true	https://github.com/AIDC-AI/Marco-LLM/tree/main/DetectRL-X	null	0.65
9a40506df5b1fd87b997230f0ad56e6e2788699893db8d2d58fd01261e14e558	[ "arxiv", "semantic_scholar" ]	Covert Multi-bit LLM Watermarking: An Information Theory and Coding Approach	We study the problem of multi-bit watermarking for large language models (LLMs). We introduce a block-autoregressive model inspired by multi-token prediction, in which the encoder has limited non-causal access to token distributions within each block. This formulation enables an information-theoretic characterization o...	[ "Sidong Guo", "Tyler Kann", "Teodora Baluta", "Matthieu R. Bloch" ]	[ "cs.IT" ]	[ "Computer Science", "Mathematics" ]	2026-05-15T00:00:00	https://arxiv.org/abs/2605.16709	https://arxiv.org/pdf/2605.16709v1	2605.16709	null	0	0	false	null	null	0.35
b3b61f840442e93b481613262513b0c64ad8bdb5ad73a29d58f18799e3962f7a	[ "arxiv", "semantic_scholar" ]	Watermarking Should Be Treated as a Monitoring Primitive	Watermarking is widely proposed for provenance, attribution, and safety monitoring in generative models, yet is typically evaluated only under adversaries who attempt to evade detection or induce false positives at the level of individual samples. We argue that watermarking should be treated as a monitoring primitive, ...	[ "Toluwani Aremu", "Nils Lukas", "Jie Zhang" ]	[ "cs.CR", "cs.AI", "cs.CY", "cs.LG" ]	[ "Computer Science" ]	2026-05-13T00:00:00	https://arxiv.org/abs/2605.13095	https://arxiv.org/pdf/2605.13095v2	2605.13095	null	0	0	false	null	null	0.35
0e8664d9252d4b5298d76247b714c1716ba78f806af6dfbed9398e50a7acc3ca	[ "arxiv", "semantic_scholar" ]	Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts	The rapid advancement of large language models (LLMs) has made machine-generated text increasingly difficult to distinguish from human-written text. While recent studies explore leveraging internal representations of language models to uncover deeper detection signals, these raw features often exhibit substantial overl...	[ "Luxu Liang", "Xiang Li" ]	[ "stat.AP", "cs.LG" ]	[ "Mathematics", "Computer Science" ]	2026-05-13T00:00:00	https://arxiv.org/abs/2605.12890	https://arxiv.org/pdf/2605.12890v1	2605.12890	null	0	0	false	null	null	0.35
6d02aeedee986662b53f910d52fc4147a50fda2b26c27bed914c50ab7ae830cb	[ "arxiv", "semantic_scholar" ]	Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark	With LLM watermarking already being deployed commercially, practical applications increasingly require multibit watermarks that encode more complex payloads, such as user IDs or timestamps, into the generated text. In this work, we propose a fundamentally new approach for multibit watermarking: introducing binomial enc...	[ "Thibaud Gloaguen", "Robin Staab", "Mark Vero", "Martin Vechev" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-05-12T00:00:00	https://arxiv.org/abs/2605.11653	https://arxiv.org/pdf/2605.11653v1	2605.11653	null	0	0	false	null	null	0.35
032e7a3748c84479b2fca01b7cd0ac2ff760d52cdafc48eee23de9e2d0d6bc55	[ "arxiv", "semantic_scholar" ]	Sequential Behavioral Watermarking for LLM Agents	LLM-based agents act through sequences of executable decisions, but their trajectories provide little evidence of which agent or policy produced them, making provenance, ownership, and unauthorized reuse difficult to establish from observed behavior alone. This motivates watermarking signals embedded directly into agen...	[ "Hyeseon An", "Shinwoo Park", "Dongsu Kim", "Yo-Sub Han" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-05-11T00:00:00	https://arxiv.org/abs/2605.11036	https://arxiv.org/pdf/2605.11036v1	2605.11036	null	1	0	false	null	null	0.35
20fd8ec7d8e33e5e6111b698031d69aa6d647e0f60a4e3c48d34eede2e970f3b	[ "arxiv", "semantic_scholar" ]	PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection	With the proliferation of AI-generated images, digital watermarking has become an essential safeguard for protecting intellectual property and mitigating malicious exploitation. Recent works on semantic watermarking have enabled efficient copyright protection for diffusion models. However, the dependence of semantic wa...	[ "Minh Quoc Duong", "Chun Tong Lei", "Chun Pong Lau" ]	[ "cs.CV", "cs.LG" ]	[ "Computer Science" ]	2026-05-10T00:00:00	https://arxiv.org/abs/2605.09319	https://arxiv.org/pdf/2605.09319v1	2605.09319	null	0	0	false	null	null	0.35
0a143f7f6fc1fef46d6e5300b518c2aaf937fdd20114e8d0da6f645391cc4471	[ "arxiv", "semantic_scholar" ]	PASA: A Principled Embedding-Space Watermarking Approach for LLM-Generated Text under Semantic-Invariant Attacks	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 watermar...	[ "Zhenxin Ai", "Haiyun He" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-05-09T00:00:00	https://arxiv.org/abs/2605.10977	https://arxiv.org/pdf/2605.10977v2	2605.10977	null	2	0	false	null	null	0.35
ff7ccda7e8d638d126a4ae32a650d4d61301a8135a1026b85bb52a7aab4198dd	[ "arxiv", "semantic_scholar" ]	Vaporizer: Breaking Watermarking Schemes for Large Language Model Outputs	In this paper, we investigate the recent state-of-the-art schemes for watermarking large language models (LLMs) outputs. These techniques are claimed to be robust, scalable and production-grade, aimed at promoting responsible usage of LLMs. We analyse the effectiveness of these watermarking techniques against an extens...	[ "Jonathan Hong Jin Ng", "Anh Tu Ngo", "Anupam Chattopadhyay" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-05-08T00:00:00	https://arxiv.org/abs/2605.07481	https://arxiv.org/pdf/2605.07481v1	2605.07481	null	0	0	false	null	null	0.35
592eb7ec2d3028f2ecb0294b7cfea8adf50268ad15b0924c61d0e8082715c9e8	[ "arxiv", "semantic_scholar" ]	Dataset Watermarking for Closed LLMs with Provable Detection	Large language models (LLMs) are pre-trained and post-trained on vast amounts of loosely curated data, raising the possibility that these models may have been trained on proprietary datasets or the same benchmarks used for evaluation. This motivates the need for dataset watermarking: designing datasets such that traini...	[ "Pengrun Huang", "Kamalika Chaudhuri", "Yu-Xiang Wang" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-05-07T00:00:00	https://arxiv.org/abs/2605.06865	https://arxiv.org/pdf/2605.06865v1	2605.06865	null	0	0	false	null	null	0.35
0b944d0f1f0077bce631482f6dbb4a588020f8961c760f03e926da116d131221	[ "arxiv", "semantic_scholar" ]	SWAN: Semantic Watermarking with Abstract Meaning Representation	We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). In contrast to existing watermarking methods, which typically encode signatures by adjusting token sel...	[ "Ziping Ye", "Gourab Dey", "Christos Christodoulopoulos", "Charith Peris", "Anil Ramakrishna", "Weitong Ruan", "Aram Galstyan", "Kai-Wei Chang", "Rahul Gupta", "Ninareh Mehrabi" ]	[ "cs.CL", "cs.AI", "cs.CR", "cs.CY" ]	[ "Computer Science" ]	2026-05-05T00:00:00	https://arxiv.org/abs/2605.04305	https://arxiv.org/pdf/2605.04305v1	2605.04305	null	0	0	false	null	null	0.35
bc024fbe3457f2b6ddd74f6ceda93128672ebc0217668f59f5466c3a1af87429	[ "arxiv", "semantic_scholar" ]	VertMark: A Unified Training-Free Robust Watermarking Framework for Vertical Domain Pre-trained Language Models	With the application of vertical domain pre-trained language models (VPLMs) in specialized fields such as medical, finance, and law, model parameters and inference capabilities have become important digital assets. Achieving traceable copyright verification for VPLMs has become an urgent challenge. Existing copyright v...	[ "Cong Kong", "Xin Cheng", "Zhaoxia Yin", "Shuai Li", "Jie Zhang", "Weiming Zhang" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-05-04T00:00:00	https://arxiv.org/abs/2605.02557	https://arxiv.org/pdf/2605.02557v1	2605.02557	null	0	0	false	null	null	0.35
66afa5b0724d9227f2e36a5ccc01895b722778bd5bb3c36cb8dfd03f4f7e4acd	[ "arxiv", "semantic_scholar" ]	MelShield: Robust Mel-Domain Audio Watermarking for Provenance Attribution of AI Generated Synthesized Speech	In this paper, we propose MelShield, a robust, in-generation, keyed audio watermarking framework that embeds identifiable signals into AI-generated audio for copyright protection and reliable attribution. Specifically, MelShield operates in the Mel-spectrogram domain during the generation process, targeting intermediat...	[ "Yutong Jin", "Qi Li", "Lingshuang Liu", "Jianbing Ni" ]	[ "cs.SD", "cs.CR" ]	[ "Computer Science" ]	2026-05-02T00:00:00	https://arxiv.org/abs/2605.01515	https://arxiv.org/pdf/2605.01515v1	2605.01515	null	0	0	false	null	null	0.35
2254027052ab225ce170429991c90356342adcab58fefaaf46e37a881dbebaa5	[ "arxiv", "semantic_scholar" ]	LLM Output Detectability and Task Performance Can be Jointly Optimized	Detecting machine-generated text is essential for transparency and accountability when deploying large language models (LLMs). Among detection approaches, watermarking is a statistically reliable method by design -- it embeds detectable signals into LLM outputs by biasing their token distributions. However, it has been...	[ "Koshiro Saito", "Ryuto Koike", "Masahiro Kaneko", "Naoaki Okazaki" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-05-02T00:00:00	https://arxiv.org/abs/2605.01350	https://arxiv.org/pdf/2605.01350v1	2605.01350	null	0	0	false	null	null	0.35
2eb2a364d7e1fc34d606cf0efb4be268413a6cebdbd5dd7aa8b828ce36b8fb15	[ "arxiv", "semantic_scholar" ]	VOW: Verifiable and Oblivious Watermark Detection for Large Language Models	Large Language Model (LLM) watermarking is crucial for establishing the provenance of machine-generated text, but most existing methods rely on a centralized trust model. This model forces users to reveal potentially sensitive text to a provider for detection and offers no way to verify the integrity of the result. Whi...	[ "Xiaokun Luan", "Yihao Zhang", "Pengcheng Su", "Feiran Lei", "Meng Sun" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-04-30T00:00:00	https://arxiv.org/abs/2604.27666	https://arxiv.org/pdf/2604.27666v1	2604.27666	10.48550/arXiv.2604.27666	0	0	false	null	arXiv.org	0.55
9a634c817f2ee3db98fe38696b6382faa2e21f7cf77f8cc286b9aa1b666bebd0	[ "arxiv", "semantic_scholar" ]	The Forensic Cost of Watermark Removal: From Dedicated Attacks to Image Editing	Current watermark removal methods are evaluated on two axes: attack success rate and perceptual quality. We show this is insufficient. While state-of-the-art attacks successfully degrade the watermark signal without visible distortion, they leave distinct statistical artifacts that betray the removal attempt. We name t...	[ "Gautier Evennou", "Ewa Kijak" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-04-28T00:00:00	https://arxiv.org/abs/2604.25491	https://arxiv.org/pdf/2604.25491v2	2604.25491	10.48550/arXiv.2604.25491	0	0	false	null	null	0.35
9f08bcbc44782546c1db57aa54e87d26079791288eab4d2754f0d0eb5cc455f2	[ "arxiv", "semantic_scholar" ]	LAVA: Layered Audio-Visual Anti-tampering Watermarking for Robust Deepfake Detection and Localization	Proactive watermarking offers a promising approach for deepfake tamper detection and localization in short-form videos. However, existing methods often decouple audio and visual evidence and assume that watermark signals remain reliable under real-world degradations, making tamper localization vulnerable to multimodal ...	[ "Bokang Zeng", "Zheng Gao", "Xiaoyu Li", "Xiaoyan Feng", "Jiaojiao Jiang" ]	[ "cs.CV" ]	[ "Computer Science" ]	2026-04-27T00:00:00	https://arxiv.org/abs/2604.23957	https://arxiv.org/pdf/2604.23957v1	2604.23957	10.48550/arXiv.2604.23957	0	0	false	null	arXiv.org	0.55
a9b6da561a143eb3f416769def3f1fb8a007548ace22b8f8bc1d41b1d2608bb3	[ "arxiv", "semantic_scholar" ]	An Empirical Evaluation of Locally Deployed LLMs for Bug Detection in Python Code	Large language models (LLMs) have demonstrated strong performance on a wide range of software engineering tasks, including code generation and analysis. However, most prior work relies on cloud-based models or specialized hardware, limiting practical applicability in privacy-sensitive or resource-constrained environmen...	[ "Jelena Ilić Vulićević" ]	[ "cs.SE", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2026-04-25T00:00:00	https://arxiv.org/abs/2604.23361	https://arxiv.org/pdf/2604.23361v1	2604.23361	10.48550/arXiv.2604.23361	0	0	true	https://github.com/insajder/llm-bug-detection	arXiv.org	0.85
ad5f79f88cc2a2b5965910887a98684b3eecd3fdf0fe00cb88fd7f817cdc4357	[ "arxiv", "semantic_scholar" ]	SSG: Logit-Balanced Vocabulary Partitioning for LLM Watermarking	Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing approaches, the KGW scheme is particularly attractive due to its versatility, efficiency, and effectiveness in natural language generation. However, KGW's effectiveness degra...	[ "Chenxi Gu", "Xiaoning Du", "John Grundy" ]	[ "cs.CR", "cs.AI", "cs.CL" ]	[ "Computer Science" ]	2026-04-24T00:00:00	https://arxiv.org/abs/2604.22438	https://arxiv.org/pdf/2604.22438v1	2604.22438	10.48550/arXiv.2604.22438	0	0	false	null	arXiv.org	0.55
50379d67da5c615e6fc488cf481133d6a05b39ec6117008640e5793231f53a78	[ "arxiv", "semantic_scholar" ]	MATRIX: Multi-Layer Code Watermarking via Dual-Channel Constrained Parity-Check Encoding	Code Large Language Models (Code LLMs) have revolutionized software development but raised critical concerns regarding code provenance, copyright protection, and security. Existing code watermarking approaches suffer from two fundamental limitations: black-box methods either exhibit detectable syntactic patterns vulner...	[ "Yuqing Nie", "Chong Wang", "Guosheng Xu", "Guoai Xu", "Chenyu Wang", "Haoyu Wang", "Kailong Wang" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-04-17T00:00:00	https://arxiv.org/abs/2604.16001	https://arxiv.org/pdf/2604.16001v1	2604.16001	10.48550/arXiv.2604.16001	0	0	false	null	arXiv.org	0.5466
50e0c9bf572ca411d09cb4158d63b9b2957b2fea2b578e30b2c68070d7ce7d7f	[ "arxiv", "semantic_scholar" ]	LLM-Enhanced Log Anomaly Detection: A Comprehensive Benchmark of Large Language Models for Automated System Diagnostics	System log anomaly detection is critical for maintaining the reliability of large-scale software systems, yet traditional methods struggle with the heterogeneous and evolving nature of modern log data. Recent advances in Large Language Models (LLMs) offer promising new approaches to log understanding, but a systematic ...	[ "Disha Patel" ]	[ "cs.LG", "cs.SE" ]	[ "Computer Science" ]	2026-04-14T00:00:00	https://arxiv.org/abs/2604.12218	https://arxiv.org/pdf/2604.12218v1	2604.12218	10.48550/arXiv.2604.12218	0	0	true	https://github.com/disha8611/llm-log-anomaly-benchmark	arXiv.org	0.8394
d38f1696f980e190ce54ecf0171a88bc7c8f6923b2a25cb1ee5eb4f893290a4a	[ "arxiv", "semantic_scholar" ]	Geometry-Aware Localized Watermarking for Copyright Protection in Embedding-as-a-Service	Embedding-as-a-Service (EaaS) has become an important semantic infrastructure for natural language and multimedia applications, but it is highly vulnerable to model stealing and copyright infringement. Existing EaaS watermarking methods face a fundamental robustness--utility--verifiability tension: trigger-based method...	[ "Zhimin Chen", "Xiaojie Liang", "Wenbo Xu", "Yuxuan Liu", "Wei Lu" ]	[ "cs.CR", "cs.CL" ]	[ "Computer Science" ]	2026-04-13T00:00:00	https://arxiv.org/abs/2604.11344	https://arxiv.org/pdf/2604.11344v1	2604.11344	10.48550/arXiv.2604.11344	0	0	false	null	arXiv.org	0.542
e00f8bab30895afbcafc2fe85a7eac1cfcda3021e2126690a6222572af573694	[ "arxiv", "semantic_scholar" ]	RLSpoofer: A Lightweight Evaluator for LLM Watermark Spoofing Resilience	Large language model (LLM) watermarking has emerged as a promising approach for detecting and attributing AI-generated text, yet its robustness to black-box spoofing remains insufficiently evaluated. Existing evaluation methods often demand extensive datasets and white-box access to algorithmic internals, limiting thei...	[ "Hanbo Huang", "Xuan Gong", "Yiran Zhang", "Hao Zheng", "Shiyu Liang" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-04-13T00:00:00	https://arxiv.org/abs/2604.11546	https://arxiv.org/pdf/2604.11546v1	2604.11546	10.48550/arXiv.2604.11546	0	0	false	null	arXiv.org	0.542
cf342dfe3a4be37cd35dce309c0c7d3256763c076f05fb8ddfd0b4b67e71fe56	[ "arxiv", "semantic_scholar" ]	AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection	Detecting AI-generated text is becoming increasingly challenging as modern language models approach human-level fluency and can evade detectors that rely on surface statistics or likelihood-based signals. We propose \textsc{AEyeDE}, an attribution-driven approach to human-AI authorship detection that leverages model at...	[ "Aria Nourbakhsh", "Adelaide Danilov", "Christoph Schommer", "Salima Lamsiyah" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2026-04-13T00:00:00	https://arxiv.org/abs/2606.00016	https://arxiv.org/pdf/2606.00016v1	2606.00016	null	0	0	false	null	null	0.3449
c3b8067198f414a444b1ea61438e6e62c5fd38276190cabcaff9fc4f6689fed2	[ "arxiv", "semantic_scholar" ]	Can we Watermark Low-Entropy LLM Outputs?	A recent and exciting thread of work focuses on developing methods for watermarking the output of large language models (LLMs). We focus on provably undetectable watermarking-that is, schemes that do not alter the output distribution of the LLM, yet enable embedding a watermark in the output that identifies the output ...	[ "Noam Mazor", "Andrew Morgan", "Rafael Pass" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-04-13T00:00:00	https://arxiv.org/abs/2604.12051	https://arxiv.org/pdf/2604.12051v1	2604.12051	10.48550/arXiv.2604.12051	0	0	false	null	arXiv.org	0.542
dda340236b6198f6455547abf724b9e5aa70a040e03315429d38bdbfa1bd7fe9	[ "arxiv", "semantic_scholar" ]	Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection	The rapid growth of generative AI has introduced new challenges in content moderation and digital forensics. In particular, benign AI-generated images can be paired with harmful or misleading text, creating difficult-to-detect misuse. This contextual misuse undermines the traditional moderation framework and complicate...	[ "Xinlei Guan", "David Arosemena", "Tejaswi Dhandu", "Kuan Huang", "Meng Xu", "Miles Q. Li", "Bingyu Shen", "Ruiyang Qin", "Umamaheswara Rao Tida", "Boyang Li" ]	[ "cs.CV", "cs.AI", "cs.CR", "cs.ET" ]	[ "Computer Science" ]	2026-04-12T00:00:00	https://arxiv.org/abs/2604.10460	https://arxiv.org/pdf/2604.10460v1	2604.10460	10.48550/arXiv.2604.10460	1	0	true	https://github.com/bli1/steganography	arXiv.org	0.8358
ee4b56799d4133eaf7d30bd07bc129f867140df387c0458ac4a639833d420f3d	[ "arxiv", "semantic_scholar" ]	Towards Robust Content Watermarking Against Removal and Forgery Attacks	Generated contents have raised serious concerns about copyright protection, image provenance, and credit attribution. A potential solution for these problems is watermarking. Recently, content watermarking for text-to-image diffusion models has been studied extensively for its effective detection utility and robustness...	[ "Yifan Zhu", "Yihan Wang", "Xiao-Shan Gao" ]	[ "cs.CV", "cs.LG" ]	[ "Computer Science" ]	2026-04-08T00:00:00	https://arxiv.org/abs/2604.06662	https://arxiv.org/pdf/2604.06662v1	2604.06662	10.48550/arXiv.2604.06662	0	0	false	null	arXiv.org	0.5363
1824c2c8c39cb85cfc87a50472bf50bfd438b52933c0b711e14fae1a5a35a3a8	[ "arxiv", "semantic_scholar" ]	XMark: Reliable Multi-Bit Watermarking for LLM-Generated Texts	Multi-bit watermarking has emerged as a promising solution for embedding imperceptible binary messages into Large Language Model (LLM)-generated text, enabling reliable attribution and tracing of malicious usage of LLMs. Despite recent progress, existing methods still face key limitations: some become computationally i...	[ "Jiahao Xu", "Rui Hu", "Olivera Kotevska", "Zikai Zhang" ]	[ "cs.CL", "cs.AI", "cs.CR" ]	[ "Computer Science" ]	2026-04-06T00:00:00	https://arxiv.org/abs/2604.05242	https://arxiv.org/pdf/2604.05242v2	2604.05242	10.48550/arXiv.2604.05242	1	0	true	https://github.com/JiiahaoXU/XMark	arXiv.org	0.8252
68383d51c4436131a66f383128c6e75d415e5ccdc9081dbdefc7c95987c252b9	[ "arxiv", "semantic_scholar" ]	Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection	The misuse of large language models (LLMs) requires precise detection of synthetic text. Existing works mainly follow binary or ternary classification settings, which can only distinguish pure human/LLM text or collaborative text at best. This remains insufficient for the nuanced regulation, as the LLM-polished human t...	[ "Yang Li", "Qiang Sheng", "Zhengjia Wang", "Yehan Yang", "Danding Wang", "Juan Cao" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-04-06T00:00:00	https://arxiv.org/abs/2604.04932	https://arxiv.org/pdf/2604.04932v3	2604.04932	10.48550/arXiv.2604.04932	0	0	false	null	arXiv.org	0.534
ed7ef2f4cf1ab823fc10c7f7c0dc97c4784aa23fdd1dd041ed0bfdfa496b3f7e	[ "arxiv", "semantic_scholar" ]	Refined Detection for Gumbel Watermarking	We propose a simple detection mechanism for the Gumbel watermarking scheme proposed by Aaronson (2022). The new mechanism is proven to be near-optimal in a problem-dependent sense among all model-agnostic watermarking schemes under the assumption that the next-token distribution is sampled i.i.d.	[ "Tor Lattimore" ]	[ "cs.LG", "cs.CR", "stat.ML" ]	[ "Computer Science", "Mathematics" ]	2026-03-31T00:00:00	https://arxiv.org/abs/2603.30017	https://arxiv.org/pdf/2603.30017v1	2603.30017	10.48550/arXiv.2603.30017	0	0	false	null	arXiv.org	0.5271
8829790a8224f83556513de6480b683d80d3613dbf3c49547b1e0a8cd5af02d4	[ "arxiv", "semantic_scholar" ]	EnsemJudge: Enhancing Reliability in Chinese LLM-Generated Text Detection through Diverse Model Ensembles	Large Language Models (LLMs) are widely applied across various domains due to their powerful text generation capabilities. While LLM-generated texts often resemble human-written ones, their misuse can lead to significant societal risks. Detecting such texts is an essential technique for mitigating LLM misuse, and many ...	[ "Zhuoshang Wang", "Yubing Ren", "Guoyu Zhao", "Xiaowei Zhu", "Hao Li", "Yanan Cao" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-03-30T00:00:00	https://arxiv.org/abs/2603.27949	https://arxiv.org/pdf/2603.27949v1	2603.27949	10.1007/978-981-95-3352-7_23	0	0	true	https://github.com/johnsonwangzs/MGT-Mini	Natural Language Processing and Chinese Computing	0.8128
7b0c855d608aca7f9e5edcded020ae7f5e02208bf781cf65bec85f1e4c6268a7	[ "arxiv", "semantic_scholar" ]	Detecting the Machine: A Comprehensive Benchmark of AI-Generated Text Detectors Across Architectures, Domains, and Adversarial Conditions	The rapid proliferation of large language models (LLMs) has created an urgent need for robust and generalizable detectors of machine-generated text. Existing benchmarks typically evaluate a single detector on a single dataset under ideal conditions, leaving open questions about cross-domain transfer, cross-LLM generali...	[ "Madhav S. Baidya", "S. S. Baidya", "Chirag Chawla" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2026-03-18T00:00:00	https://arxiv.org/abs/2603.17522	https://arxiv.org/pdf/2603.17522v1	2603.17522	10.48550/arXiv.2603.17522	0	0	true	https://github.com/MadsDoodle/Human-and-LLM-Generated-Text-Detectability-under-Adversarial-Humanization	arXiv.org	0.7916
48b42f14e37d92b124a1caec4028772cb9b001f4ca1a0c69bd53e31f6ad52c59	[ "arxiv", "semantic_scholar" ]	Rethinking LLM Watermark Detection in Black-Box Settings: A Non-Intrusive Third-Party Framework	While watermarking serves as a critical mechanism for LLM provenance, existing secret-key schemes tightly couple detection with injection, requiring access to keys or provider-side scheme-specific detectors for verification. This dependency creates a fundamental barrier for real-world governance, as independent auditin...	[ "Zhuoshang Wang", "Yubing Ren", "Yanan Cao", "Fang Fang", "Xiaoxue Li", "Li Guo" ]	[ "cs.CR", "cs.CL" ]	[ "Computer Science" ]	2026-03-16T00:00:00	https://arxiv.org/abs/2603.14968	https://arxiv.org/pdf/2603.14968v2	2603.14968	10.48550/arXiv.2603.14968	0	0	false	null	arXiv.org	0.5099
87a23cb6facc7c58fac40a863b28b803cfadad1ccd326b759165da5387db3046	[ "arxiv", "semantic_scholar" ]	AWPD: Frequency Shield Network for Agnostic Watermark Presence Detection	Invisible watermarks, as an essential technology for image copyright protection, have been widely deployed with the rapid development of social media and AIGC. However, existing invisible watermark detection heavily relies on prior knowledge of specific algorithms, leading to limited detection capabilities for ``unknow...	[ "Xiang Ao", "Yilin Du", "Zidan Wang", "Mengru Chen", "Siyang Lu" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2026-03-06T00:00:00	https://arxiv.org/abs/2603.06723	https://arxiv.org/pdf/2603.06723v3	2603.06723	10.48550/arXiv.2603.06723	0	0	false	null	arXiv.org	0.4984
df4411ad50fc64a36969913f43963e2c74a2448709155099c5adc364b7e2089a	[ "arxiv", "semantic_scholar" ]	NOTAI.AI: Explainable Detection of Machine-Generated Text via Curvature and Feature Attribution	We present NOTAI.AI, an explainable framework for machine-generated text detection that extends Fast-DetectGPT by integrating curvature-based signals with neural and stylometric features in a supervised setting. The system combines 17 interpretable features, including Conditional Probability Curvature, ModernBERT detec...	[ "Oleksandr Marchenko Breneur", "Adelaide Danilov", "Aria Nourbakhsh", "Salima Lamsiyah" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-03-05T00:00:00	https://arxiv.org/abs/2603.05617	https://arxiv.org/pdf/2603.05617v1	2603.05617	10.48550/arXiv.2603.05617	1	0	false	null	arXiv.org	0.4973
072e7cab60e02c6bebf196f1d0924c91b687f31152d60144aa4516bd1eab1047	[ "arxiv", "semantic_scholar" ]	On Google's SynthID-Text LLM Watermarking System: Theoretical Analysis and Empirical Validation	Google's SynthID-Text, the first ever production-ready generative watermark system for large language model, designs a novel Tournament-based method that achieves the state-of-the-art detectability for identifying AI-generated texts. The system's innovation lies in: 1) a new Tournament sampling algorithm for watermarki...	[ "Romina Omidi", "Yun Dong", "Binghui Wang" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-03-03T00:00:00	https://arxiv.org/abs/2603.03410	https://arxiv.org/pdf/2603.03410v2	2603.03410	10.48550/arXiv.2603.03410	0	0	true	null	arXiv.org	0.765
3849d7ef8ec9a0a4f3d66e122e8911ab7ab4a9e8fde4bc881d0301fc1f835906	[ "arxiv", "semantic_scholar" ]	SKeDA: A Generative Watermarking Framework for Text-to-video Diffusion Models	The rise of text-to-video generation models has raised growing concerns over content authenticity, copyright protection, and malicious misuse. Watermarking serves as an effective mechanism for regulating such AI-generated content, where high fidelity and strong robustness are particularly critical. Recent generative im...	[ "Yang Yang", "Xinze Zou", "Zehua Ma", "Han Fang", "Weiming Zhang" ]	[ "cs.CV", "cs.AI", "cs.CR" ]	[ "Computer Science" ]	2026-02-27T00:00:00	https://arxiv.org/abs/2603.00194	https://arxiv.org/pdf/2603.00194v1	2603.00194	10.48550/arXiv.2603.00194	0	0	false	null	arXiv.org	0.4904
f85fc68cefaa07008d75c0f98259a727584d739b7fe0c54bd0f40621ff89c2db	[ "arxiv", "semantic_scholar" ]	Breaking Semantic-Aware Watermarks via LLM-Guided Coherence-Preserving Semantic Injection	Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has increasingly been integrated into diffusion models to support reliable provenance tracking and forgery prevention for web content. Traditional noise-layer-based watermarking, h...	[ "Zheng Gao", "Xiaoyu Li", "Zhicheng Bao", "Xiaoyan Feng", "Jiaojiao Jiang" ]	[ "cs.LG", "cs.CR", "cs.CV" ]	[ "Computer Science" ]	2026-02-25T00:00:00	https://arxiv.org/abs/2602.21593	https://arxiv.org/pdf/2602.21593v1	2602.21593	10.1145/3774904.3792912	2	0	false	null	The Web Conference	0.4881
5490fbc6a6cdf364602f2c655015831df6f59a60e10755490f83afa534fe0bf5	[ "arxiv", "semantic_scholar" ]	Attention to Neural Plagiarism: Diffusion Models Can Plagiarize Your Copyrighted Images!	In this paper, we highlight a critical threat posed by emerging neural models: data plagiarism. We demonstrate how modern neural models (e.g., diffusion models) can replicate copyrighted images, even when protected by advanced watermarking techniques. To expose vulnerabilities in copyright protection and facilitate fut...	[ "Zihang Zou", "Boqing Gong", "Liqiang Wang" ]	[ "cs.CV", "cs.CY" ]	[ "Computer Science" ]	2026-02-25T00:00:00	https://arxiv.org/abs/2603.00150	https://arxiv.org/pdf/2603.00150v1	2603.00150	10.1109/ICCV51701.2025.01817	3	0	true	https://github.com/zzzucf/Neural-Plagiarism	IEEE International Conference on Computer Vision	0.7544
410ad0ac97ec9b87619747ec0b16047209deef03e1efb4d8d4219089819d7fba	[ "arxiv", "semantic_scholar" ]	SiGRRW: A Single-Watermark Robust Reversible Watermarking Framework with Guiding Strategy	Robust reversible watermarking (RRW) enables copyright protection for images while overcoming the limitation of distortion introduced by watermark itself. Current RRW schemes typically employ a two-stage framework, which fails to achieve simultaneous robustness and reversibility within a single watermarking, and functi...	[ "Zikai Xu", "Bin Liu", "Weihai Li", "Lijunxian Zhang", "Nenghai Yu" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-02-22T00:00:00	https://arxiv.org/abs/2602.19097	https://arxiv.org/pdf/2602.19097v1	2602.19097	10.48550/arXiv.2602.19097	0	0	false	null	arXiv.org	0.4847
1091a7757b1ae278d9d86c066047a550f7195976b85659bdbdc784943632a47a	[ "arxiv", "semantic_scholar" ]	Watermarking LLM Agent Trajectories	LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial task design, high-capacity model generation, and manual filtering. Despite the high cost of creating these datasets, existing literature has overlooked copyright protection for ...	[ "Wenlong Meng", "Chen Gong", "Terry Yue Zhuo", "Fan Zhang", "Kecen Li", "Zheng Liu", "Zhou Yang", "Chengkun Wei", "Wenzhi Chen" ]	[ "cs.CR", "cs.CL" ]	[ "Computer Science" ]	2026-02-21T00:00:00	https://arxiv.org/abs/2602.18700	https://arxiv.org/pdf/2602.18700v2	2602.18700	10.48550/arXiv.2602.18700	1	1	false	null	arXiv.org	0.4835
62bf09ce5b1b5eb7543ed3aa946d4bdc6096ccebf1420835a8b13bdda1024cd6	[ "arxiv", "semantic_scholar" ]	Unforgeable Watermarks for Language Models via Robust Signatures	Language models now routinely produce text that is difficult to distinguish from human writing, raising the need for robust tools to verify content provenance. Watermarking has emerged as a promising countermeasure, with existing work largely focused on model quality preservation and robust detection. However, current ...	[ "Huijia Lin", "Kameron Shahabi", "Min Jae Song" ]	[ "cs.CR", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2026-02-17T00:00:00	https://arxiv.org/abs/2602.15323	https://arxiv.org/pdf/2602.15323v1	2602.15323	10.48550/arXiv.2602.15323	0	0	false	null	IACR Cryptology ePrint Archive	0.479
20ecbccfe4aaa67dbb234bb73b049cf933cca3cc8b99c4ea3a1d6d73f6e01125	[ "arxiv", "semantic_scholar" ]	Online LLM watermark detection via e-processes	Watermarking for large language models (LLMs) has emerged as an effective tool for distinguishing AI-generated text from human-written content. Statistically, watermark schemes induce dependence between generated tokens and a pseudo-random sequence, reducing watermark detection to a hypothesis testing problem on indepe...	[ "Weijie Su", "Ruodu Wang", "Zinan Zhao" ]	[ "stat.ME", "stat.ML" ]	[ "Mathematics" ]	2026-02-15T00:00:00	https://arxiv.org/abs/2602.14286	https://arxiv.org/pdf/2602.14286v4	2602.14286	null	0	0	false	null	null	0.3033
03e7938aaf5ac706a1158da1f0aa092ee3d31382d2a4a242d96a930019ce4e43	[ "arxiv", "semantic_scholar" ]	DWBench: Holistic Evaluation of Watermark for Dataset Copyright Auditing	The surging demand for large-scale datasets in deep learning has heightened the need for effective copyright protection, given the risks of unauthorized use to data owners. Although the dataset watermark technique holds promise for auditing and verifying usage, existing methods are hindered by inconsistent evaluations,...	[ "Xiao Ren", "Xinyi Yu", "Linkang Du", "Min Chen", "Yuanchao Shu", "Zhou Su", "Yunjun Gao", "Zhikun Zhang" ]	[ "cs.CR" ]	[ "Computer Science" ]	2026-02-14T00:00:00	https://arxiv.org/abs/2602.13541	https://arxiv.org/pdf/2602.13541v1	2602.13541	10.48550/arXiv.2602.13541	0	0	true	null	arXiv.org	0.7349
1661655be0ef91f438e90479c30fcf31d4bd067ed1fbc15956f4865045505a3e	[ "arxiv", "semantic_scholar" ]	GPTZero: Robust Detection of LLM-Generated Texts	While historical considerations surrounding text authenticity revolved primarily around plagiarism, the advent of large language models (LLMs) has introduced a new challenge: distinguishing human-authored from AI-generated text. This shift raises significant concerns, including the undermining of skill evaluations, the...	[ "George Alexandru Adam", "Alexander Cui", "Edwin Thomas", "Emily Napier", "Nazar Shmatko", "Jacob Schnell", "Jacob Junqi Tian", "Alekhya Dronavalli", "Edward Tian", "Dongwon Lee" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-02-13T00:00:00	https://arxiv.org/abs/2602.13042	https://arxiv.org/pdf/2602.13042v1	2602.13042	10.48550/arXiv.2602.13042	9	2	false	null	arXiv.org	0.4744
c32d662fe037f1e0dea439c102bd7cd6fd9f88d00c0c7bdddc43f7125189e71d	[ "arxiv", "semantic_scholar" ]	Both Topology and Text Matter: Revisiting LLM-guided Out-of-Distribution Detection on Text-attributed Graphs	Text-attributed graphs (TAGs) associate nodes with textual attributes and graph structure, enabling GNNs to jointly model semantic and structural information. Although effective on in-distribution (ID) data, GNNs often fail on out-of-distribution (OOD) nodes with unseen textual or structural patterns, producing overcon...	[ "Yinlin Zhu", "Di Wu", "Xu Wang", "Guocong Quan", "Miao Hu" ]	[ "cs.LG" ]	[ "Computer Science" ]	2026-02-12T00:00:00	https://arxiv.org/abs/2602.11641	https://arxiv.org/pdf/2602.11641v2	2602.11641	10.48550/arXiv.2602.11641	0	0	false	null	arXiv.org	0.4732
63dee319eadf7fcdd2d29b4803412d5e9087ec92e49d961e9d27ef4fdddc3b2c	[ "arxiv", "semantic_scholar" ]	Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detection	The goal of the PAN workshop is to advance computational stylometry and text forensics via objective and reproducible evaluation. In 2026, we run the following five tasks: (1) Voight-Kampff Generative AI Detection, particularly in mixed and obfuscated authorship scenarios, (2) Text Watermarking, a new task that aims to...	[ "Janek Bevendorff", "Maik Fröbe", "André Greiner-Petter", "Andreas Jakoby", "Maximilian Mayerl", "Preslav Nakov", "Henry Plutz", "Martin Potthast", "Benno Stein", "Minh Ngoc Ta", "Yuxia Wang", "Eva Zangerle" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-02-09T00:00:00	https://arxiv.org/abs/2602.09147	https://arxiv.org/pdf/2602.09147v1	2602.09147	10.48550/arXiv.2602.09147	0	0	false	null	European Conference on Information Retrieval	0.4698
1f426ee5e8f83ddf69f242f3bd3d619e9ad63c1507a672a8f56ae5b1c337d8d4	[ "arxiv", "semantic_scholar" ]	A Unified Framework for LLM Watermarks	LLM watermarks allow tracing AI-generated texts by inserting a detectable signal into their generated content. Recent works have proposed a wide range of watermarking algorithms, each with distinct designs, usually built using a bottom-up approach. Crucially, there is no general and principled formulation for LLM water...	[ "Thibaud Gloaguen", "Robin Staab", "Nikola Jovanović", "Martin Vechev" ]	[ "cs.CR", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2026-02-06T00:00:00	https://arxiv.org/abs/2602.06754	https://arxiv.org/pdf/2602.06754v1	2602.06754	10.48550/arXiv.2602.06754	1	0	false	null	arXiv.org	0.4664
5a9e21ecc0ab090eb6c04702fa0c7f4bad274ea6cc2dd9922e193658e48afa87	[ "arxiv", "semantic_scholar" ]	ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport	Watermarking is an important tool for promoting the responsible use of large language models (LLMs). Existing watermarks insert a signal into generated tokens that either flags LLM-generated text (zero-bit watermarking) or encodes more complex messages (multi-bit watermarking). Though a number of recent approaches inse...	[ "Atefeh Gilani", "Sajani Vithana", "Carol Xuan Long", "Oliver Kosut", "Lalitha Sankar", "Flavio P. Calmon" ]	[ "cs.LG", "cs.AI", "cs.IT" ]	[ "Computer Science", "Mathematics" ]	2026-02-06T00:00:00	https://arxiv.org/abs/2602.07235	https://arxiv.org/pdf/2602.07235v2	2602.07235	null	1	1	false	null	null	0.2968
90996422c0aa881cb6101df7afbfd724c8ee2bbcbc705d3a1a3e55aef9df379f	[ "arxiv", "semantic_scholar" ]	Cost-Aware Model Selection for Text Classification: Multi-Objective Trade-offs Between Fine-Tuned Encoders and LLM Prompting in Production	Large language models (LLMs) such as GPT-4o and Claude Sonnet 4.5 have demonstrated strong capabilities in open-ended reasoning and generative language tasks, leading to their widespread adoption across a broad range of NLP applications. However, for structured text classification problems with fixed label spaces, mode...	[ "Alberto Andres Valdes Gonzalez" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-02-06T00:00:00	https://arxiv.org/abs/2602.06370	https://arxiv.org/pdf/2602.06370v1	2602.06370	10.48550/arXiv.2602.06370	0	0	false	null	arXiv.org	0.4664
06d01005a00a6776ba2a297ce11900e1b5e5241503206a46fff349a0eb366cd3	[ "arxiv", "semantic_scholar" ]	Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks	We present Copyright Detective, the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. The system treats copyright infringement versus compliance as an evidence discovery process rather than a static classification task due to the complex nature of copy...	[ "Guangwei Zhang", "Jianing Zhu", "Cheng Qian", "Neil Gong", "Rada Mihalcea", "Zhaozhuo Xu", "Jingrui He", "Jiaqi Ma", "Yun Huang", "Chaowei Xiao", "Bo Li", "Ahmed Abbasi", "Dongwon Lee", "Heng Ji", "Denghui Zhang" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-02-05T00:00:00	https://arxiv.org/abs/2602.05252	https://arxiv.org/pdf/2602.05252v2	2602.05252	10.48550/arXiv.2602.05252	1	0	false	null	arXiv.org	0.4652
7297fdb0b1f1c8b8c47e4e3635c01395f6cd611f368630c1a625745e6793303e	[ "arxiv", "semantic_scholar" ]	WorldCup Sampling for Multi-bit LLM Watermarking	As large language models (LLMs) generate increasingly human-like text, watermarking has emerged as a promising solution for reliable attribution beyond mere detection. While multi-bit watermarking enables richer provenance encoding, existing approaches typically extend zero-bit watermarking schemes by introducing stati...	[ "Yidan Wang", "Yubing Ren", "Yanan Cao", "Li Guo" ]	[ "cs.CL", "cs.CR" ]	[ "Computer Science" ]	2026-02-02T00:00:00	https://arxiv.org/abs/2602.01752	https://arxiv.org/pdf/2602.01752v2	2602.01752	10.48550/arXiv.2602.01752	0	0	false	null	arXiv.org	0.4618
d83bdc90cfb63e1e70999f5adb3ac5181d1929e8b14fd001447d0bc41ee30d28	[ "arxiv", "semantic_scholar" ]	MirrorMark: Generalizable Mirrored Sampling for Multi-bit LLM Watermarking	As large language models (LLMs) become integral to applications such as question answering and content creation, reliable content attribution has become increasingly important. Watermarking is a promising approach, but most existing methods either provide only binary signals or achieve multi-bit embedding by distorting...	[ "Ya Jiang", "Massieh Kordi Boroujeny", "Surender Suresh Kumar", "Kai Zeng" ]	[ "cs.CR", "cs.AI" ]	[ "Computer Science" ]	2026-01-29T00:00:00	https://arxiv.org/abs/2601.22246	https://arxiv.org/pdf/2601.22246v3	2601.22246	null	0	0	false	null	null	0.2909
9bedcca919714526d8e5071ea1064ec919769fde5a231286f2ff229395ae2b65	[ "arxiv", "semantic_scholar" ]	Variation is the Key: A Variation-Based Framework for LLM-Generated Text Detection	Detecting text generated by large language models (LLMs) is crucial but challenging. Existing detectors depend on impractical assumptions, such as white-box settings, or solely rely on text-level features, leading to imprecise detection ability. In this paper, we propose a simple but effective and practical LLM-generat...	[ "Xuecong Li", "Xiaohong Li", "Qiang Hu", "Yao Zhang", "Junjie Wang" ]	[ "cs.AI", "cs.CL" ]	[ "Computer Science" ]	2026-01-27T00:00:00	https://arxiv.org/abs/2602.13226	https://arxiv.org/pdf/2602.13226v1	2602.13226	10.48550/arXiv.2602.13226	0	0	false	null	arXiv.org	0.4549
94f4a3c3fb18236d3fc97765068902cdbed71d5125525641649e8e5fd323b888	[ "arxiv", "semantic_scholar" ]	On the Effectiveness of LLM-Specific Fine-Tuning for Detecting AI-Generated Text	The rapid progress of large language models has enabled the generation of text that closely resembles human writing, creating challenges for authenticity verification in education, publishing, and digital security. Detecting AI-generated text has therefore become a crucial technical and ethical issue. This paper presen...	[ "Michał Gromadzki", "Anna Wróblewska", "Agnieszka Kaliska" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2026-01-27T00:00:00	https://arxiv.org/abs/2601.20006	https://arxiv.org/pdf/2601.20006v1	2601.20006	10.48550/arXiv.2601.20006	0	0	true	null	arXiv.org	0.703
ec1cffae932cd51406ec96fbfe04ce78dd9a2df67f2b282cc6fde30174c0a006	[ "arxiv", "semantic_scholar" ]	LLM-42: Enabling Determinism in LLM Inference with Verified Speculation	In LLM inference, the same prompt may yield different outputs across different runs. At the system level, this non-determinism arises from floating-point non-associativity combined with dynamic batching and GPU kernels whose reduction orders vary with batch size. A straightforward way to eliminate non-determinism is to...	[ "Raja Gond", "Aditya K Kamath", "Ramachandran Ramjee", "Ashish Panwar" ]	[ "cs.LG", "cs.AI", "cs.DC" ]	[ "Computer Science" ]	2026-01-25T00:00:00	https://arxiv.org/abs/2601.17768	https://arxiv.org/pdf/2601.17768v2	2601.17768	10.48550/arXiv.2601.17768	7	0	true	https://github.com/microsoft/llm-42	arXiv.org	0.6995
a6fe6403a5d41699ca0d6bb58d566459deaf5a8d01233f321d6f16897bd276b8	[ "arxiv", "semantic_scholar" ]	Zero-Shot Embedding Drift Detection: A Lightweight Defense Against Prompt Injections in LLMs	Prompt injection attacks have become an increasing vulnerability for LLM applications, where adversarial prompts exploit indirect input channels such as emails or user-generated content to circumvent alignment safeguards and induce harmful or unintended outputs. Despite advances in alignment, even state-of-the-art LLMs...	[ "Anirudh Sekar", "Mrinal Agarwal", "Rachel Sharma", "Akitsugu Tanaka", "Jasmine Zhang", "Arjun Damerla", "Kevin Zhu" ]	[ "cs.CR", "cs.AI", "cs.CL" ]	[ "Computer Science" ]	2026-01-18T00:00:00	https://arxiv.org/abs/2601.12359	https://arxiv.org/pdf/2601.12359v1	2601.12359	10.48550/arXiv.2601.12359	1	0	false	null	arXiv.org	0.4446
e32397d4c2d8b008252cf9cdede0f0bbea55a587354ffb62bc2c797ae60dce58	[ "arxiv", "semantic_scholar" ]	LR-DWM: Efficient Watermarking for Diffusion Language Models	Watermarking (WM) is a critical mechanism for detecting and attributing AI-generated content. Current WM methods for Large Language Models (LLMs) are predominantly tailored for autoregressive (AR) models: They rely on tokens being generated sequentially, and embed stable signals within the generated sequence based on t...	[ "Ofek Raban", "Ethan Fetaya", "Gal Chechik" ]	[ "cs.CL" ]	[ "Computer Science" ]	2026-01-18T00:00:00	https://arxiv.org/abs/2601.12376	https://arxiv.org/pdf/2601.12376v1	2601.12376	10.48550/arXiv.2601.12376	0	0	false	null	arXiv.org	0.4446
66cf3e105fe6f0eefbc846e038fcb3e5f04502960d7e604243760c2ec0274f53	[ "arxiv", "semantic_scholar" ]	Dissecting Judicial Reasoning in U.S. Copyright Damage Awards	Judicial reasoning in copyright damage awards poses a core challenge for computational legal analysis. Although federal courts follow the 1976 Copyright Act, their interpretations and factor weightings vary widely across jurisdictions. This inconsistency creates unpredictability for litigants and obscures the empirical...	[ "Pei-Chi Lo", "Thomas Y. Lu" ]	[ "cs.IR", "cs.CL" ]	[ "Computer Science" ]	2026-01-14T00:00:00	https://arxiv.org/abs/2601.09459	https://arxiv.org/pdf/2601.09459v1	2601.09459	10.48550/arXiv.2601.09459	0	0	false	null	arXiv.org	0.44
565b391e275cea415bb8e9c343c5434cf865833acd2b785e1dd92819630ca597	[ "arxiv", "semantic_scholar" ]	BanglaLorica: Design and Evaluation of a Robust Watermarking Algorithm for Large Language Models in Bangla Text Generation	As large language models (LLMs) are increasingly deployed for text generation, watermarking has become essential for authorship attribution, intellectual property protection, and misuse detection. While existing watermarking methods perform well in high-resource languages, their robustness in low-resource languages rem...	[ "Amit Bin Tariqul", "A N M Zahid Hossain Milkan", " Sahab-Al-Chowdhury", "Syed Rifat Raiyan", "Hasan Mahmud", "Md Kamrul Hasan" ]	[ "cs.CL", "cs.AI" ]	[ "Computer Science" ]	2026-01-08T00:00:00	https://arxiv.org/abs/2601.04534	https://arxiv.org/pdf/2601.04534v1	2601.04534	10.48550/arXiv.2601.04534	1	0	false	null	arXiv.org	0.4331
4efe7cd60d600f61d645178cb7c952114d7801dc4f9c72678100cb29f6e988cf	[ "arxiv", "semantic_scholar" ]	Distilling the Thought, Watermarking the Answer: A Principle Semantic Guided Watermark for Large Reasoning Models	Reasoning Large Language Models (RLLMs) excelling in complex tasks present unique challenges for digital watermarking, as existing methods often disrupt logical coherence or incur high computational costs. Token-based watermarking techniques can corrupt the reasoning flow by applying pseudo-random biases, while semanti...	[ "Shuliang Liu", "Xingyu Li", "Hongyi Liu", "Dong Fang", "Yibo Yan", "Bingchen Duan", "Qi Zheng", "Lingfeng Su", "Xuming Hu" ]	[ "cs.AI" ]	[ "Computer Science" ]	2026-01-08T00:00:00	https://arxiv.org/abs/2601.05144	https://arxiv.org/pdf/2601.05144v2	2601.05144	10.48550/arXiv.2601.05144	2	0	false	null	arXiv.org	0.4331
ebcd0293dfa17eff8e9dbcd6700d6271f33aac295949e21545da5a5d112adfe7	[ "arxiv", "semantic_scholar" ]	Smark: A Watermark for Text-to-Speech Diffusion Models via Discrete Wavelet Transform	Text-to-Speech (TTS) diffusion models generate high-quality speech, which raises challenges for the model intellectual property protection and speech tracing for legal use. Audio watermarking is a promising solution. However, due to the structural differences among various TTS diffusion models, existing watermarking me...	[ "Yichuan Zhang", "Chengxin Li", "Yujie Gu" ]	[ "cs.SD", "cs.AI", "cs.CR" ]	[ "Computer Science" ]	2025-12-21T00:00:00	https://arxiv.org/abs/2512.18791	https://arxiv.org/pdf/2512.18791v1	2512.18791	10.48550/arXiv.2512.18791	0	0	false	null	arXiv.org	0.4125
f6aa1cac4d216e9a53ba250ccf7415e21019407eb64d1184d0c8f64250c93815	[ "arxiv", "semantic_scholar" ]	From Essence to Defense: Adaptive Semantic-aware Watermarking for Embedding-as-a-Service Copyright Protection	Benefiting from the superior capabilities of large language models in natural language understanding and generation, Embeddings-as-a-Service (EaaS) has emerged as a successful commercial paradigm on the web platform. However, prior studies have revealed that EaaS is vulnerable to imitation attacks. Existing methods pro...	[ "Hao Li", "Yubing Ren", "Yanan Cao", "Yingjie Li", "Fang Fang", "Xuebin Wang" ]	[ "cs.CR", "cs.CL" ]	[ "Computer Science" ]	2025-12-18T00:00:00	https://arxiv.org/abs/2512.16439	https://arxiv.org/pdf/2512.16439v1	2512.16439	10.48550/arXiv.2512.16439	1	0	false	null	arXiv.org	0.4091
01cecbe589adddcb99f26edeb18a634b69f07d8122b5783fd5450ce85f23178d	[ "arxiv", "semantic_scholar" ]	How Good is Post-Hoc Watermarking With Language Model Rephrasing?	Generation-time text watermarking embeds statistical signals into text for traceability of AI-generated content. We explore post-hoc watermarking where an LLM rewrites existing text while applying generation-time watermarking, to protect copyrighted documents, or detect their use in training or RAG via watermark radi...	[ "Pierre Fernandez", "Tom Sander", "Hady Elsahar", "Hongyan Chang", "Tomáš Souček", "Valeriu Lacatusu", "Tuan Tran", "Sylvestre-Alvise Rebuffi", "Alexandre Mourachko" ]	[ "cs.CR", "cs.CL" ]	[ "Computer Science" ]	2025-12-18T00:00:00	https://arxiv.org/abs/2512.16904	https://arxiv.org/pdf/2512.16904v2	2512.16904	10.48550/arXiv.2512.16904	1	0	true	https://github.com/facebookresearch/textseal	arXiv.org	0.6322
be269b45dd4de32e43f13f95e4bc205ee5b509da7ca542e4d37a60c8a78ac083	[ "arxiv", "semantic_scholar" ]	DualGuard: Dual-stream Large Language Model Watermarking Defense against Paraphrase and Spoofing Attack	With the rapid development of cloud-based services, large language models have become increasingly accessible through various web platforms. However, this accessibility has also led to growing risks of model abuse. LLM watermarking has emerged as an effective approach to mitigate such misuse and protect intellectual pr...	[ "Hao Li", "Yubing Ren", "Yanan Cao", "Yingjie Li", "Fang Fang", "Shi Wang", "Li Guo" ]	[ "cs.CR", "cs.CL" ]	[ "Computer Science" ]	2025-12-18T00:00:00	https://arxiv.org/abs/2512.16182	https://arxiv.org/pdf/2512.16182v2	2512.16182	10.48550/arXiv.2512.16182	0	0	false	null	arXiv.org	0.4091
72727470771403d147dc906de7c18eb738eb8c7f494fbefe08332d4738774dcf	[ "arxiv", "semantic_scholar" ]	Perturb Your Data: Paraphrase-Guided Training Data Watermarking	Training data detection is critical for enforcing copyright and data licensing, as Large Language Models (LLM) are trained on massive text corpora scraped from the internet. We present SPECTRA, a watermarking approach that makes training data reliably detectable even when it comprises less than 0.001% of the training c...	[ "Pranav Shetty", "Mirazul Haque", "Petr Babkin", "Zhiqiang Ma", "Xiaomo Liu", "Manuela Veloso" ]	[ "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2025-12-18T00:00:00	https://arxiv.org/abs/2512.17075	https://arxiv.org/pdf/2512.17075v1	2512.17075	10.1609/aaai.v40i39.40575	0	0	false	null	AAAI Conference on Artificial Intelligence	0.4091
8bc6b224bf899db545fd9345cad8673c2ece33f1b9ea3bb57af515d7c0ba1794	[ "arxiv", "semantic_scholar" ]	Where is the Watermark? Interpretable Watermark Detection at the Block Level	Recent advances in generative AI have enabled the creation of highly realistic digital content, raising concerns around authenticity, ownership, and misuse. While watermarking has become an increasingly important mechanism to trace and protect digital media, most existing image watermarking schemes operate as black box...	[ "Maria Bulychev", "Neil G. Marchant", "Benjamin I. P. Rubinstein" ]	[ "cs.CV", "cs.AI" ]	[ "Computer Science" ]	2025-12-17T00:00:00	https://arxiv.org/abs/2512.14994	https://arxiv.org/pdf/2512.14994v1	2512.14994	10.1109/WACV61042.2026.00716	0	0	false	null	IEEE Workshop/Winter Conference on Applications of Computer Vision	0.4079
1e0667c3d6b572a514fe4c10a174b6c748451a8f055477bb2040cd0cbbeef5c9	[ "arxiv", "semantic_scholar" ]	Remotely Detectable Robot Policy Watermarking	The success of machine learning for real-world robotic systems has created a new form of intellectual property: the trained policy. This raises a critical need for novel methods that verify ownership and detect unauthorized, possibly unsafe misuse. While watermarking is established in other domains, physical policies p...	[ "Michael Amir", "Manon Flageat", "Amanda Prorok" ]	[ "cs.RO", "cs.CR", "cs.LG", "eess.SY" ]	[ "Computer Science", "Engineering" ]	2025-12-17T00:00:00	https://arxiv.org/abs/2512.15379	https://arxiv.org/pdf/2512.15379v1	2512.15379	10.48550/arXiv.2512.15379	0	0	false	null	arXiv.org	0.4079
93416c2eedf3318a1181bbc94cd01fa7f2e0b4b0a8553c6a8cbe71f3660fecc2	[ "arxiv", "semantic_scholar" ]	Security and Detectability Analysis of Unicode Text Watermarking Methods Against Large Language Models	Securing digital text is becoming increasingly relevant due to the widespread use of large language models. Individuals' fear of losing control over data when it is being used to train such machine learning models or when distinguishing model-generated output from text written by humans. Digital watermarking provides a...	[ "Malte Hellmeier" ]	[ "cs.CR", "cs.AI", "cs.LG" ]	[ "Computer Science" ]	2025-12-15T00:00:00	https://arxiv.org/abs/2512.13325	https://arxiv.org/pdf/2512.13325v1	2512.13325	10.48550/arXiv.2512.13325	0	0	false	null	International Conference on Information Systems Security and Privacy	0.4056
23e82335268f2deb0f5eda060528e03ea35e42a52b4a538d21a17a767cb70d90	[ "arxiv", "semantic_scholar" ]	Ideal Attribution and Faithful Watermarks for Language Models	We introduce ideal attribution mechanisms, a formal abstraction for reasoning about attribution decisions over strings. At the core of this abstraction lies the ledger, an append-only log of the prompt-response interaction history between a model and its user. Each mechanism produces deterministic decisions based on th...	[ "Min Jae Song", "Kameron Shahabi" ]	[ "cs.CR", "cs.LG", "stat.ML" ]	[ "Computer Science", "Mathematics" ]	2025-12-07T00:00:00	https://arxiv.org/abs/2512.07038	https://arxiv.org/pdf/2512.07038v1	2512.07038	10.48550/arXiv.2512.07038	0	0	false	null	arXiv.org	0.3965
3bd9ad163494ff9d455586bb8d0f2d2f88d009c5a117d637ccc5d56f5a07d17c	[ "arxiv", "semantic_scholar" ]	Love First, Know Later: Persona-Based Romantic Compatibility Through LLM Text World Engines	We propose Love First, Know Later: a paradigm shift in computational matching that simulates interactions first, then assesses compatibility. Instead of comparing static profiles, our framework leverages LLMs as text world engines that operate in dual capacity-as persona-driven agents following behavioral policies and ...	[ "Haoyang Shang", "Zhengyang Yan", "Xuan Liu" ]	[ "cs.HC", "cs.CL", "cs.LG" ]	[ "Computer Science" ]	2025-12-04T00:00:00	https://arxiv.org/abs/2512.11844	https://arxiv.org/pdf/2512.11844v2	2512.11844	10.48550/arXiv.2512.11844	1	0	false	null	arXiv.org	0.393
21317edea16c21d6cdc5fedb9ef26be22c056927ed81a147d1a711b6218a09d6	[ "arxiv", "semantic_scholar" ]	DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution	In the age of advanced large language models (LLMs), the boundaries between human and AI-generated text are becoming increasingly blurred. We address the challenge of segmenting mixed-authorship text, that is identifying transition points in text where authorship shifts from human to AI or vice-versa, a problem with cr...	[ "L. D. M. S. Sai Teja", "N. Siva Gopala Krishna", "Ufaq Khan", "Muhammad Haris Khan", "Atul Mishra" ]	[ "cs.CL" ]	[ "Computer Science" ]	2025-12-04T00:00:00	https://arxiv.org/abs/2512.04838	https://arxiv.org/pdf/2512.04838v2	2512.04838	10.48550/arXiv.2512.04838	3	1	false	null	Conference of the European Chapter of the Association for Computational Linguistics	0.393
abdff4d33d1f70040ca9a14d1f8d3f35a8fc23df736afeecea798b628f0f5c85	[ "arxiv", "semantic_scholar" ]	MarkTune: Improving the Quality-Detectability Trade-off in Open-Weight LLM Watermarking	Watermarking aims to embed hidden signals in generated text that can be reliably detected when given access to a secret key. Open-weight language models pose acute challenges for such watermarking schemes because the inference-time interventions that dominate contemporary approaches cannot be enforced once model weight...	[ "Yizhou Zhao", "Zhiwei Steven Wu", "Adam Block" ]	[ "cs.LG", "cs.AI", "cs.CR" ]	[ "Computer Science" ]	2025-12-03T00:00:00	https://arxiv.org/abs/2512.04044	https://arxiv.org/pdf/2512.04044v1	2512.04044	10.48550/arXiv.2512.04044	0	0	false	null	arXiv.org	0.3919
37d1ed2a3757911b903263ee3235801df997faf5e5ebe090274407b2d6e9b2ed	[ "arxiv", "semantic_scholar" ]	WaterSearch: Exploring Seed Pooling for Improving the Quality-Detectability Trade-off in LLM Watermarking	Watermarking acts as a critical safeguard in text generated by Large Language Models (LLMs). By embedding identifiable signals into model outputs, watermarking enables reliable attribution and enhances the security of machine-generated content. Existing approaches typically embed signals by manipulating token generatio...	[ "Yukang Lin", "Jiahao Shao", "Shuoran Jiang", "Wentao Zhu", "Bingjie Lu", "Xiangping Wu", "Joanna Siebert", "Qingcai Chen" ]	[ "cs.CL" ]	[ "Computer Science" ]	2025-11-30T00:00:00	https://arxiv.org/abs/2512.00837	https://arxiv.org/pdf/2512.00837v3	2512.00837	null	0	0	true	https://github.com/Yukang-Lin/WaterSearch}{https://github.com/Yukang-Lin/WaterSearch}	null	0.4591
e0fd3c962ab2e6dcc190b6aa7243c049550b65a04b73d9cc26fb6c479921241a	[ "arxiv", "semantic_scholar" ]	Can Protective Watermarking Safeguard the Copyright of 3D Gaussian Splatting?	3D Gaussian Splatting (3DGS) has emerged as a powerful representation for 3D scenes, widely adopted due to its exceptional efficiency and high-fidelity visual quality. Given the significant value of 3DGS assets, recent works have introduced specialized watermarking schemes to ensure copyright protection and ownership v...	[ "Wenkai Huang", "Yijia Guo", "Gaolei Li", "Lei Ma", "Hang Zhang", "Liwen Hu", "Jiazheng Wang", "Jianhua Li", "Tiejun Huang" ]	[ "cs.CV" ]	[ "Computer Science" ]	2025-11-27T00:00:00	https://arxiv.org/abs/2511.22262	https://arxiv.org/pdf/2511.22262v2	2511.22262	10.48550/arXiv.2511.22262	2	0	true	https://github.com/insightlab-CG-3DV/GSPure	AAAI Conference on Artificial Intelligence	0.595
ccd6accdc452e492b97a78489e0ce4a58f08da8cedefa9056938120142ca6eaf	[ "arxiv", "semantic_scholar" ]	TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs	Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the context of fake news detection, remains significantly underexplored. One of the key challenges is the scarcity of large-scale, realistic, and ...	[ "Kay Liu", "Yuwei Han", "Haoyan Xu", "Henry Peng Zou", "Yue Zhao", "Philip S. Yu" ]	[ "cs.SI", "cs.CL" ]	[ "Computer Science" ]	2025-11-26T00:00:00	https://arxiv.org/abs/2511.21624	https://arxiv.org/pdf/2511.21624v1	2511.21624	10.48550/arXiv.2511.21624	1	0	true	https://github.com/kayzliu/tagfn	arXiv.org	0.5932
30eda908ce61a82b1914ad83796a2a979d210a527e73a93eda8c083f8c82bf73	[ "arxiv", "semantic_scholar" ]	Copyright Detection in Large Language Models: An Ethical Approach to Generative AI Development	The widespread use of Large Language Models (LLMs) raises critical concerns regarding the unauthorized inclusion of copyrighted content in training data. Existing detection frameworks, such as DE-COP, are computationally intensive, and largely inaccessible to independent creators. As legal scrutiny increases, there is ...	[ "David Szczecina", "Senan Gaffori", "Edmond Li" ]	[ "cs.AI" ]	[ "Computer Science" ]	2025-11-25T00:00:00	https://arxiv.org/abs/2511.20623	https://arxiv.org/pdf/2511.20623v1	2511.20623	10.48550/arXiv.2511.20623	0	0	true	null	arXiv.org	0.5915
8150a33852928fd9171c8ba65bdc7502b8165422b0a46d31f33dbe806108d374	[ "arxiv", "semantic_scholar" ]	Who Owns the Knowledge? Copyright, GenAI, and the Future of Academic Publishing	The integration of generative artificial intelligence (GenAI) and large language models (LLMs) into scientific research and higher education presents a paradigm shift, offering revolutionizing opportunities while simultaneously raising profound ethical, legal, and regulatory questions. This study examines the complex i...	[ "Dmitry Kochetkov" ]	[ "cs.DL", "cs.AI", "cs.CY" ]	[ "Computer Science" ]	2025-11-24T00:00:00	https://arxiv.org/abs/2511.21755	https://arxiv.org/pdf/2511.21755v2	2511.21755	10.48550/arXiv.2511.21755	1	0	false	null	arXiv.org	0.3816
f5f4cfb96f6025e7917831c555adf9b315bb5ed2df3fd2129fdf45aed9b79a6e	[ "arxiv", "semantic_scholar" ]	RegionMarker: A Region-Triggered Semantic Watermarking Framework for Embedding-as-a-Service Copyright Protection	Embedding-as-a-Service (EaaS) is an effective and convenient deployment solution for addressing various NLP tasks. Nevertheless, recent research has shown that EaaS is vulnerable to model extraction attacks, which could lead to significant economic losses for model providers. For copyright protection, existing methods ...	[ "Shufan Yang", "Zifeng Cheng", "Zhiwei Jiang", "Yafeng Yin", "Cong Wang", "Shiping Ge", "Yuchen Fu", "Qing Gu" ]	[ "cs.CL", "cs.CR" ]	[ "Computer Science" ]	2025-11-17T00:00:00	https://arxiv.org/abs/2511.13329	https://arxiv.org/pdf/2511.13329v1	2511.13329	10.48550/arXiv.2511.13329	1	1	false	null	AAAI Conference on Artificial Intelligence	0.3735
f4a171fd8f949b14c651d51d739e844c963b690ceb8d399deb02eed661b779a7	[ "arxiv", "semantic_scholar" ]	LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning	Anomaly detection on attributed graphs plays an essential role in applications such as fraud detection, intrusion monitoring, and misinformation analysis. However, text-attributed graphs (TAGs), in which node information is expressed in natural language, remain underexplored, largely due to the absence of standardized ...	[ "Haoyan Xu", "Ruizhi Qian", "Zhengtao Yao", "Ziyi Liu", "Li Li", "Yuqi Li", "Yanshu Li", "Wenqing Zheng", "Daniele Rosa", "Daniel Barcklow", "Senthil Kumar", "Jieyu Zhao", "Yue Zhao" ]	[ "cs.LG", "cs.AI" ]	[ "Computer Science" ]	2025-11-16T00:00:00	https://arxiv.org/abs/2511.17584	https://arxiv.org/pdf/2511.17584v1	2511.17584	10.48550/arXiv.2511.17584	1	0	false	null	arXiv.org	0.3724

End of preview. Expand in Data Studio

LLM Watermarking & Copyright Detection Papers — FineSet

A research-paper dataset on LLM Watermarking & Copyright Detection Papers, assembled, deduplicated, and quality-scored by FineSet from arXiv and Semantic Scholar.

📸 This is a dated snapshot — generated 2026-06-19. It is not auto-updated. Research on LLM Watermarking & Copyright Detection Papers moves fast — new papers land on arXiv every week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓

Why this dataset

Quality-scored: quality_score float (0–1), blends citations with recency + code/venue signals — filter out the noise
Papers with code: 130 flagged via has_code — find reproducible work fast
Deduplicated: arXiv + Semantic Scholar cross-referenced, duplicate records merged
Clean JSONL: 442 records, one per line, normalized fields — no encoding garbage

Dataset details

Records: 442
Date range: 2022–2026
Snapshot date: 2026-06-19 (frozen — see note above)
Sources: arXiv, Semantic Scholar (cross-referenced, duplicates merged)
arXiv categories: cs.CL, cs.CR, cs.LG
Quality scoring: citations + recency + code/venue blend, 0–1 (p50=0.335, p90=0.55)
Format: JSONL, one record per line

Fields

Field	Type	Description
id	string	Deterministic SHA256 record id
sources	list	Which sources contributed (`arxiv`, `semantic_scholar`)
title	string	Paper title
abstract	string	Full abstract
authors	list	Author names
categories	list	arXiv category codes
fields_of_study	list	Semantic Scholar field tags
published_date	string	ISO 8601 date
url	string	arXiv abstract URL
pdf_url	string\|null	Open-access PDF if available
arxiv_id	string\|null	arXiv identifier
doi	string\|null	DOI if available
citation_count	int	Citation count (Semantic Scholar)
influential_citation_count	int	Influential citations (Semantic Scholar)
has_code	bool	Code repo detected in the arXiv comment
code_url	string\|null	GitHub URL if detected
venue	string\|null	Publication venue
quality_score	float	0–1, blended (citations + recency + code/venue)

Quality score methodology

quality_score = max(impact, freshness), clamped to [0, 1], where:

impact = max( log10(citations+1)/4 , log10(influential_citations+1)/2 ) — realized impact (0.5 at 100 citations, ~0.75 at 1,000, 1.0 at 10,000+).
freshness = recency × (0.35 + 0.30·has_code + 0.20·has_venue) — a baseline for recent papers (so a strong paper published this week isn't scored 0 just for lacking citations), where recency is 1.0 for papers ≤60 days old and decays linearly to 0 by ~18 months.

Old highly-cited papers score on impact; brand-new papers score on freshness; old uncited papers score ~0. Useful for filtering training data by quality, not just age.

👉 Want this on YOUR topic, updated daily?

This snapshot is frozen at 2026-06-19. The live FineSet pipeline keeps a dataset like this refreshed every day on whatever topic you describe — new papers in, dedup and quality scoring automatic, export as JSONL/Parquet or push straight to the Hub.

Tell me the topic you'd want and I'll run the pipeline on it — open a discussion on this dataset, it's free and it's how I decide what to build next.

→ fineset.io — describe what you want to train on, get a dataset. Early-access waitlist open (referral skip available).

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