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This paper focuses on investigation of confidential documents leaks in the form of screen photographs. Proposed approach does not try to prevent leak in the first place but rather aims to determine source of the leak. Method works by…

Protecting the Intellectual Property rights of DNN models is of primary importance prior to their deployment. So far, the proposed methods either necessitate changes to internal model parameters or the machine learning pipeline, or they…

密码学与安全 · 计算机科学 2022-06-23 Kassem Kallas , Teddy Furon

Semantic watermarking methods enable the direct integration of watermarks into the generation process of latent diffusion models by only modifying the initial latent noise. One line of approaches building on Gaussian Shading relies on…

密码学与安全 · 计算机科学 2025-03-17 Jonas Thietke , Andreas Müller , Denis Lukovnikov , Asja Fischer , Erwin Quiring

Machine learning (ML) models are applied in an increasing variety of domains. The availability of large amounts of data and computational resources encourages the development of ever more complex and valuable models. These models are…

密码学与安全 · 计算机科学 2021-12-09 Franziska Boenisch

Digital multimedia watermarking technology was suggested in the last decade to embed copyright information in digital objects such images, audio and video. However, the increasing use of relational database systems in many real-life…

数据库 · 计算机科学 2013-04-29 Jun Ziang Pinn , A. Fr. Zung

Watermarking techniques offer a promising way to identify machine-generated content via embedding covert information into the contents generated from language models (LMs). However, the robustness of the watermarking schemes has not been…

计算与语言 · 计算机科学 2025-07-04 Ruibo Chen , Yihan Wu , Junfeng Guo , Heng Huang

Digital watermarking enables protection against copyright infringement of images. Although existing methods embed watermarks imperceptibly and demonstrate robustness against attacks, they typically lack resilience against geometric…

多媒体 · 计算机科学 2024-02-15 Hannes Mareen , Lucas Antchougov , Glenn Van Wallendael , Peter Lambert

We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking…

计算机视觉与模式识别 · 计算机科学 2022-03-24 Pierre Fernandez , Alexandre Sablayrolles , Teddy Furon , Hervé Jégou , Matthijs Douze

With the rapid development of information technology and multimedia, the use of digital data is increasing day by day. So it becomes very essential to protect multimedia information from piracy and also it is challenging. A great deal of…

多媒体 · 计算机科学 2013-07-15 Md. Maklachur Rahman

With the rapid development of deep neural networks(DNNs), many robust blind watermarking algorithms and frameworks have been proposed and achieved good results. At present, the watermark attack algorithm can not compete with the watermark…

多媒体 · 计算机科学 2023-06-23 Xinyu Li

As diffusion models (DMs) enable photorealistic image generation at unprecedented scale, watermarking techniques have become essential for provenance establishment and accountability. Existing methods face challenges: sampling-based…

计算机视觉与模式识别 · 计算机科学 2026-03-24 Hong-Hanh Nguyen-Le , Van-Tuan Tran , Thuc D. Nguyen , Nhien-An Le-Khac

Multiple watermarking technique, embedding several watermarks in one carrier, has enabled many interesting applications. In this study, a novel multiple watermarking algorithm is proposed based on the spirit of spread transform dither…

多媒体 · 计算机科学 2016-01-19 Xinchao Li , Ju Liu , Jiande Sun , Xiaohui Yang , Wei Liu

With the significant advances in deep generative models for image and video synthesis, Deepfakes and manipulated media have raised severe societal concerns. Conventional machine learning classifiers for deepfake detection often fail to cope…

计算机视觉与模式识别 · 计算机科学 2024-10-14 Aakash Varma Nadimpalli , Ajita Rattani

Latent Diffusion Models (LDMs) have established themselves as powerful tools in the rapidly evolving field of image generation, capable of producing highly realistic images. However, their widespread adoption raises critical concerns about…

密码学与安全 · 计算机科学 2026-01-28 Zhonghao Yang , Linye Lyu , Xuanhang Chang , Daojing He , YU LI

In this paper, we propose WaterMark Detection (WMD), the first invisible watermark detection method under a black-box and annotation-free setting. WMD is capable of detecting arbitrary watermarks within a given reference dataset using a…

计算机视觉与模式识别 · 计算机科学 2024-04-02 Minzhou Pan , Zhenting Wang , Xin Dong , Vikash Sehwag , Lingjuan Lyu , Xue Lin

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…

密码学与安全 · 计算机科学 2025-12-16 Malte Hellmeier

This paper proposes a novel approach towards image authentication and tampering detection by using watermarking as a communication channel for semantic information. We modify the HiDDeN deep-learning watermarking architecture to embed and…

密码学与安全 · 计算机科学 2025-10-14 Gautier Evennou , Vivien Chappelier , Ewa Kijak , Teddy Furon

Digital watermarking system is a paramount for safeguarding valuable resources and information. Digital watermarks are generally imperceptible to the human eye and ear. Digital watermark can be used in video, audio and digital images for a…

密码学与安全 · 计算机科学 2012-05-30 Md. Selim Reza , Mohammed Shafiul Alam Khan , Md. Golam Robiul Alam , Serajul Islam

With the widespread use of deep neural networks (DNNs) in many areas, more and more studies focus on protecting DNN models from intellectual property (IP) infringement. Many existing methods apply digital watermarking to protect the DNN…

密码学与安全 · 计算机科学 2022-07-11 Lina Lin , Hanzhou Wu

Diffusion large language models (dLLMs) offer faster generation than autoregressive models while maintaining comparable quality, but existing watermarking methods fail on them due to their non-sequential decoding. Unlike autoregressive…

机器学习 · 计算机科学 2025-10-06 Linyu Wu , Linhao Zhong , Wenjie Qu , Yuexin Li , Yue Liu , Shengfang Zhai , Chunhua Shen , Jiaheng Zhang