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Watermarking Neural Networks With Watermarked Images
IEEE Transactions on Circuits and Systems for Video Technology, 2021Watermarking neural networks is a quite important means to protect the intellectual property (IP) of neural networks. In this paper, we introduce a novel digital watermarking framework suitable for deep neural networks that output images as the results, in which any image outputted from a watermarked neural network must contain a certain watermark ...
Hanzhou Wu +3 more
openaire +2 more sources
A survey of Deep Neural Network watermarking techniques [PDF]
Neurocomputing, 2021 Protecting the Intellectual Property Rights (IPR) associated to Deep Neural Networks (DNNs) is a pressing need pushed by the high costs required to train such networks and by the importance that DNNs are gaining in our society.
Yue Li, Hongxia Wang, Mauro Barni
exaly +2 more sources
A Survey of Text Watermarking in the Era of Large Language Models
ACM Computing Surveys, 2023Text watermarking algorithms are crucial for protecting the copyright of textual content. Historically, their capabilities and application scenarios were limited.
Aiwei Liu +7 more
semanticscholar +1 more source
REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models
USENIX Security Symposium, 2023We present REMARK-LLM, a novel efficient, and robust watermarking framework designed for texts generated by large language models (LLMs). Synthesizing human-like content using LLMs necessitates vast computational resources and extensive datasets ...
Ruisi Zhang +3 more
semanticscholar +1 more source
Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models
IACR Cryptology ePrint Archive, 2023Watermarking generative models consists of planting a statistical signal (watermark) in a model's output so that it can be later verified that the output was generated by the given model.
Hanlin Zhang +5 more
semanticscholar +1 more source
The Multi-Watermarks Attack of DNN Watermarking
2020 4th International Conference on Advances in Image Processing, 2020Deep learning models are widely used in business scenarios and have achieved some success. It is usually time or computing consuming to build a production-level deep learning model. As a result, such models require copyright protection by watermarks. So the security of watermarks is important.
Deyin Li, Yu Yang 0005
openaire +1 more source
ARWGAN: Attention-Guided Robust Image Watermarking Model Based on GAN
IEEE Transactions on Instrumentation and Measurement, 2023In the existing deep learning-based watermarking models, extracted image features for fusing with watermark are not abundant enough and more critically, and essential features are not highlighted to be learned with the purpose of robust watermarking ...
Jiangtao Huang +5 more
semanticscholar +1 more source
Detecting Voice Cloning Attacks via Timbre Watermarking
Network and Distributed System Security Symposium, 2023Nowadays, it is common to release audio content to the public. However, with the rise of voice cloning technology, attackers have the potential to easily impersonate a specific person by utilizing his publicly released audio without any permission ...
Chang Liu +5 more
semanticscholar +1 more source
Flexible and Secure Watermarking for Latent Diffusion Model
ACM Multimedia, 2023Since the significant advancements and open-source support of latent diffusion models (LDMs) in the field of image generation, numerous researchers and enterprises start fine-tuning the pre-trained models to generate specialized images for different ...
Cheng Xiong +3 more
semanticscholar +1 more source
TrustMark: Universal Watermarking for Arbitrary Resolution Images
arXiv.org, 2023Imperceptible digital watermarking is important in copyright protection, misinformation prevention, and responsible generative AI. We propose TrustMark - a GAN-based watermarking method with novel design in architecture and spatio-spectra losses to ...
Tu Bui, S. Agarwal, J. Collomosse
semanticscholar +1 more source