Advanced Steganography and Watermarking Techniques

Neueste Paper

• Exploring Weaknesses of Generative Image Watermarks against Latent Frequency Masking

  Kirill Aistov, Khaled Abud, Irina Serzhenko, Egor Kovalev, Aleksey Yakushev, Aleksandr Akimenkov, Dmitry Obydenkov, Yury Markin, Sergey Lavrushkin, Dmitriy Vatolin, Anastasia Antsiferova · 2. Oktober 2026

  Invisible watermarking has become a central tool for tracing AI-generated images, but its robustness against adaptive removal attacks remains an open security question. We introduce Latent Frequency Masking, an attack that erases watermark evidence by replacing selected Fourier coefficients in the l…

• Semantic Watermarking for Malicious Image Manipulation Detection

  Yoonseo Kim, Seungwoo Baek, Junyoung Park · 1. Oktober 2026

  The proliferation of high-fidelity generative editing models has made it possible to inject violent or sexual content into otherwise ordinary images while preserving visual plausibility, with concrete consequences for public discourse and vulnerable populations. We propose a robust semantic watermar…

• Persistent Watermarking of Text-to-Image Models

  Dixi Yao, Kaiwen Chen, Tahseen Rabbani, Tian Li · 1. Oktober 2026

  Text-to-image (T2I) generation is gaining increasing popularity with the general public, motivating the development of reliable mechanisms for copyrighting such models given their expensive training costs. An adversary may obtain and reuse a pretrained T2I model without authorization, and then serve…

• TTMark: Pairwise Distortion-Free Watermarking Beyond Single-Token Entropy

  Ruibo Chen, Zhengmian Hu, Donghang Lu, Xuehao Cui, Georgios Milis, Yihan Wu, Jian Du, Heng Huang · 1. Oktober 2026

  Distortion-free watermarking enables reliable attribution of machine-generated text while preserving output distribution. However, existing methods operate independently on each generated token, making their detection capability fundamentally constrained by the entropy of the next-token distribution…

• WARP: A Unified Benchmark for Invisible Image Watermarking -- Robustness and Protection Against Attacks

  Khaled Abud, Aleksey Yakushev, Aleksandr Akimenkov, Irina Serzhenko, Kirill Aistov, Egor Kovalev, Dmitry Obydenkov, Sergey Lavrushkin, Anastasia Antsiferova, Dmitriy Vatolin, Yury Markin, Kirill Lukianov · 1. Oktober 2026

  Digital image watermarking is increasingly critical in media contexts, as emerging regulations and industry practices require marking AI-generated content and ensuring traceable sources to prevent manipulation or misuse. Recent advances in invisible watermarking methods highlight the need to update …

• CertMark: Distortion-Free Multi-Bit Watermarking with Certified Decoding

  Pawe{\l} Batorski, Przemys{\l}aw Spurek, Paul Swoboda · 30. September 2026

  Leading multi-bit watermarking methods for language models encode messages by biasing the model's next-token probabilities, creating a trade-off between message recovery and text quality. Their decoders typically return the highest-scoring candidate from accumulated token-level evidence, without a c…

• Beyond Semantic Narrowing: Robust and Efficient LLM Watermarking with Hamming Neighborhoods

  Zewen Sun, Tongyang Zhao, Liyao Xiang, Mingxuan Ma, Lingzhe Wang, Zhiyuan Li · 30. September 2026

  Semantic watermarking improves robustness against watermark removal attacks by embedding detectable signals into sentence-level representations. However, existing watermarking methods typically impose watermark-specific semantic preferences on generated sentences without explicitly accounting for th…

• High-Capacity Robust Medical Image Exfiltration via Neural Network Weight Replacement

  Elie Thellier (EPIONE), Huiyu Li (EPIONE), Nicholas Ayache (EPIONE), Herv\'e Delingette (EPIONE) · 29. September 2026

  Collaborative medical AI platforms allow researchers to train models on sensitive imaging data while restricting data export. However, trained models can serve as covert carriers of patient information: medical images may be encoded within model parameters and reconstructed outside the secure enviro…

• FeatMark: Feature-level Watermark Protection against Mimicry Attacks with Diffusion Models

  Haoyang Li, Ruoxi Sun, Qingqing Ye, Benjamin Zi Hao Zhao, Yaxin Xiao, Jason Xue, Haibo Hu · 28. September 2026

  Text-to-image diffusion models enable data-efficient "mimicry" attacks, wherein adversaries fine-tune the model on a handful of public photos to synthesize convincing forgeries of a target individual. A common countermeasure is to embed imperceptible, low-energy watermarks, yet recent studies show t…

• MoSign: Challenge-Response Motion-Watermark Authentication for Anonymous Virtual-Reality Users

  Xujun Che, Thomas Carr, Depeng Xu, Aidong Lu, Shuhan Yuan · 25. September 2026

  Social virtual reality (VR) creates a paradox. A user's body motion is a high-entropy biometric: head and hand trajectories alone re-identify users among tens of thousands with over $94\%$ accuracy, so anonymizing the rendered avatar is a practical necessity. Yet a user often still wants to prove th…

• An Efficient and Effective Watermarking Scheme for the Protection of the Intellectual Property Rights of Video Generative Models

  Wenhong Huang, Jianwei Fei, Benedetta Tondi, Bin Ma, Fangjun Huang · 22. September 2026

  The rapid development of video generative models (VGMs) has enabled the generation of highly realistic synthetic videos, raising concerns about the intellectual property rights (IPR) of these models. In particular, two closely related forensic tasks remain largely unaddressed: synthetic video verifi…

• Style as Cover: Deep Image Steganography via Stylized Transmission

  Qi Li, Jidong Yang, Huaike Yu, Chunpeng Wang, Suo Gao, Herbert Ho-Ching Iu, Yuantian Miao, Bin Ma, Xiao Chen · 22. September 2026

  Image steganography hides secret message within normal images, with most existing works relying on cover-preserving transmission. However, such a paradigm becomes vulnerable once the original cover is exposed or can be reliably approximated. In this paper, we propose StyleStegaNet, a stylized image …

• What Breaks Local Watermarks? A Robustness Benchmark for Local Invisible Image Watermarking

  Kai Yao, Bence Szil\'agyi, Sebesty\'en Kamp, M\'at\'e Po\'or, M\'at\'e Szilveszter, Matyas K. Zsoldos, Marc Juarez · 16. September 2026

  Local image watermarking embeds an invisible signal into selected image regions rather than spreading it across the entire image, enabling payload recovery from specific objects or regions without perceptibly altering the image. Existing studies evaluate the robustness of payload recovery and locali…

• Predictive Likelihood Ratios for Language Model Watermark Detection

  Li Ma · 16. September 2026

  Keyed watermark detection tests dependence between observed tokens and pseudorandom variables reconstructed from a secret key. Building on the pivotal framework of Li et al. (2025), we construct predictive likelihood ratios that average over uncertain probability deficits and residual-tail distribut…

• RAIN: Region-Aware Inversion Network for Semantic Watermark Extraction

  Zilai Li · 15. September 2026

  Semantic watermarks for diffusion models embed ownership information into the generative process while preserving perceptual quality, but Gaussian-Shading extraction conventionally requires multi-step diffusion inversion to recover the initial noise. Recent one-step methods show that this cost can b…

• TripPattern: A Pattern-based Text Watermarking Method for Large Language Models

  Sangjun Moon, Dasom Choi, Jingun Kwon, Hidetaka Kamigaito, Taro Watanabe, Manabu Okumura · 14. September 2026

  Text watermarking techniques have gained significant attention for identifying machine-generated text and mitigating risks from large language models (LLMs). Existing methods typically divide an LLM's vocabulary into green and red tokens, but encouraging generation toward green tokens can reduce tex…

• Watermarks Without Verification: AI Text Watermarking After the EU AI Act

  Alexander Nemecek, Vipin Chaudhary, Erman Ayday · 10. September 2026

  On August 2, 2026, the obligations of Article 50 of the EU AI Act took effect, requiring generative AI providers to mark the content their systems produce and ensure it can be detected as AI-generated. Days later, Anthropic disclosed that every Claude model released after that date embeds a watermar…

• A Trustworthy Watermarking Framework for LLM-Generated Food Safety Content

  Zhongli Fang, Yiran Chen, Lingyun Zhang, Yu Liu, Ping Chen, Xiaoyan Sun, Jun Dai · 9. September 2026

  Large language models are transforming many industries with their text generation abilities. However, their outputs can be easily tampered with, creating serious risks in critical areas such as food safety reporting. To protect the integrity and traceability of AI-generated content, this paper intro…

• A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification

  Han Zhang, Yan Wang, Guanfeng Liu, Pengfei Ding, Huaxiong Wang, Kwok-Yan Lam · 7. September 2026

  The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership infringement, such as model stealing and unauthorized misuse. To verify model ownership and prevent significant economic losses, two groups of GNN Ownership Verification (OV) methods have bee…

• AngelFingerprint: A Traceable, Explainable, and White-Box Stealthy Watermark for Text-Guided Image Editing

  Bo-Han Kung, Futa Waseda, Ching-Chun Chang, Isao Echizen, Shang-Tse Chen · 7. September 2026

  Text-guided diffusion editing raises disinformation concerns, making reliable image provenance essential. While watermarks are commonly used for this purpose, most methods carry a fixed ID that cannot explain what was changed and which prompt produced it. Furthermore, under open-source white-box acc…

• CRAW: Codec Robust Audio Watermarking

  David Chernin, Ethan Fetaya · 4. September 2026

  Recent advances in generative speech models have made it increasingly difficult to distinguish authentic from synthetic audio, enabling new forms of fraud and misinformation. Audio watermarking offers a promising defense by embedding an imperceptible signal into generated speech that can later be de…

• Flip, Don't Shuffle: Watermarking LLMs at the Speed of Inference

  Simone Ceppi, Ignacio Sanchez · 4. September 2026

  We introduce Stateless Bernoulli Watermarking (SBW), a new statistical watermark for Large Language Models that determines green list membership through independent per-token Bernoulli trials. Unlike KGW's vocabulary permutation or SynthID's multi-layer tournament, SBW requires only a single compari…

• One Prompt Is Enough: Watermark Laundering Through Foundation Image Models

  Jidong Yang, Qi Li, Wei Zong, Yang-Wai Chow, Willy Susilo, Huaike Yu, Chunpeng Wang, Suo Gao · 2. September 2026

  Invisible watermarks are typically evaluated against predefined perturbations such as compression, blur, noise, cropping, and denoising. Public foundation image models expose a distinct threat: an attacker can submit a watermarked image with a single reconstruction prompt and obtain a visually faith…

• SemTrace: Source-Grounded Semantic Signatures for Tracing LLM Exposure to Protected Documents

  Junyan Zhang, Yudong Zeng, Yongwei Huang, Zuhao Ouyang, Hong Chen, Xuming Hu · 1. September 2026

  Large language models are increasingly used to read documents and produce downstream text, creating a provenance problem when the document owner cannot control or inspect the model that performs the generation. We introduce SemTrace, a source-grounded semantic watermark for detecting whether a gener…

• OpenStamp: A Watermark for Open-Source Language Models

  Miroojin Bakshi, Saksham Rastogi, Danish Pruthi · 31. August 2026

  With the growing prevalence of large language model (LLM) generated content, watermarking is considered a promising approach for attributing text to LLMs and distinguishing it from human-written content. A prominent class of techniques embeds subtle but detectable signals in generated text by modify…