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A General Approach for Using Deep Neural Network for Digital Watermarking
[article]
2020
arXiv
pre-print
Preserved Fulltext
In this paper, we propose a general deep neural network (DNN) based watermarking method to fulfill this goal. ...
Instead of training a neural network for protecting a specific image, we train on an image set and use the trained model to protect a distinct test image set in a bulk manner. ...
Hence, neural network especially the deep neural network (DNN) that exhibits high linearity, can be a candidate approach. ...
arXiv:2003.12428v1
fatcat:a6njlwicsbgkdi7cuwf2eq6so4
An Image Compression Based Technique to Watermark a Neural Network
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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We formulate a new challenge: the integration of watermarks into neural networks through discrete cosine transform (DCT) based approach. ...
Throughout the neural networks, we also describe specifications, embedded conditions, and attack forms of watermarking. ...
We propose a conceptual framework for integrating a watermark into models of deep neural networks to safeguard copyrights and identify violation of trained models of intellectual property. ...
doi:10.35940/ijitee.d9064.029420
fatcat:hwvatnwyojgwrgj2bn52ejmwjm
Robust Black-box Watermarking for Deep NeuralNetwork using Inverse Document Frequency
[article]
2021
arXiv
pre-print
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The watermark generation scheme provides a secure watermarking method by combining Term Frequency (TF) and Inverse Document Frequency (IDF) of a particular word. ...
Recently, these Machine Learning (ML) methods, such as Deep Neural Networks (DNNs), presented exceptional achievement in implementing human-level capabilities for various predicaments, such as Natural ...
The combination of deep learning and neural networks are called deep neural networks (DNNs). ...
arXiv:2103.05590v1
fatcat:esfm4plqjjgwjobal2nuwuh7be
A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks
2020
Advances in Science, Technology and Engineering Systems
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In this paper we propose an algorithmic approach for Convolutional Neural Network (CNN) for digital watermarking which outperforms the existing frequency domain techniques in all aspects including security ...
along with the criteria in the neural networks such as conditions embedded, and types of watermarking attack. ...
We propose a conceptual framework for integrating a watermark into models of deep neural networks to safeguard copyrights and identify violation of trained models of intellectual property. ...
doi:10.25046/aj0506187
fatcat:7mv5wcjrwjdabojrjfkcpf4qqa
A Generalized Deep Neural Network Approach for Digital Watermarking Analysis
2021
IEEE Transactions on Emerging Topics in Computational Intelligence
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In this paper, we propose a deep neural network (DNN) based watermarking method to achieve this goal. ...
Instead of training a neural network for protecting a specific image, we train the network on an image dataset and generalize the trained model to protect distinct test images in a bulk manner. ...
Hence, neural network, especially the deep neural network (DNN) that exhibits high non-linearity, can be a candidate approach. ...
doi:10.1109/tetci.2021.3055520
fatcat:d4ys26fphrendiqvuayykotuoq
Detecting Digital Watermarking Image Attacks Using a Convolution Neural Network Approach
2022
Security and Communication Networks
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For this reason, a variety of watermarking systems have been investigated for a variety of purposes, including broadcast monitoring, intellectual property protection, content authentication, and copy control ...
In this paper, a deep learning method based on a convolution neural network (CNN) algorithm was proposed to detect various types of watermarking attacks, namely, median filter, Gaussian filter, salt-and-pepper ...
Artificial neural networks (ANNs) with several layers are referred to as "deep learning" or "deep neural networks." ...
doi:10.1155/2022/4408336
fatcat:nicpyvlzqzczhknvesjp2epzmm
Audio Watermarking for Security and Non-Security Applications
2022
IEEE Access
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Thus, in order to guarantee principally the protection of intellectual properties of this digital content, watermarking has appeared as a solution. ...
Moreover, we propose a first digital watermarking scheme for security copyright protection application where we have involved Neural Network architecture in the insertion and detection processes and we ...
Motivated by the great development of deep learning at the expense of the classic learning algorithms, we propose to combine the feature vector with the Deep Neural Networks to develop an audio classification ...
doi:10.1109/access.2022.3145950
fatcat:kkawupgcjrdotpag3uwhycgxou
NeuNAC: A Novel Fragile Watermarking Algorithm for Integrity Protection of Neural Networks
2021
Information Sciences
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In this paper we describe a watermarking algorithm that can protect and verify the integrity of (Deep) Neural Networks when deployed in safety critical systems, such as autonomous driving systems or monitoring ...
Neural Networks are nowadays in use in a growing number of application areas because of their excellent performances. ...
The work reflects only the authors' views; the European Commission is not responsible for any use that may be made of the information it contains. ...
doi:10.1016/j.ins.2021.06.073
fatcat:fd3bqkf6zzcq7a7jd4q6gxqwn4
DeepHider: A Multi-module and Invisibility Watermarking Scheme for Language Model
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2208.04676v1 fatcat:ji345xcso5f7xbuld5zxiy3sn4
Experiments show that the proposed scheme successfully verifies ownership with 100% watermark verification accuracy without affecting the original performance of the model, and has strong robustness and ...
approach. ...
NLP Backdoor Attacks Since most neural network models black-box watermarking is based on model backdoor, some NLP backdoor attack schemes can effectively protect the intellectual property of language models ...
arXiv:2208.04676v2
fatcat:malccpbw4ffb5lw6doq6gbnbci
Entangled Watermarks as a Defense against Model Extraction
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2002.12200v1 fatcat:rpuhis5gvvg45iydf42zhvci3y
Model owners may be concerned that valuable intellectual property can be leaked if adversaries mount model extraction attacks. ...
Our approach encourages the model to learn features for classifying data that is sampled from the task distribution and data that encodes watermarks. ...
Acknowledgments The authors would like to thank Varun Chandrasekaran for his generous help with the paper, in particular with the presentation of ideas and extensive feedback on the writing. ...
arXiv:2002.12200v2
fatcat:lz2unazz7feahiadxqsqd6rqxm
Generating Image Adversarial Examples by Embedding Digital Watermarks
[article]
2022
arXiv
pre-print
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With the increasing attention to deep neural network (DNN) models, attacks are also upcoming for such models. ...
Specifically, partial main features of the watermark image are embedded into the host image almost invisibly, aiming to tamper with and damage the recognition capabilities of the DNN models. ...
[19] for DNNs' intellectual property protection). In another work, Le et al. [20] proposed a remote watermarking extraction scheme to ensure flexibility in watermarking-enabled DNN protection. ...
arXiv:2009.05107v2
fatcat:ypujggcn4vballmagbnbspmboi
Robust watermarking with double detector-discriminator approach
[article]
2020
arXiv
pre-print
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and up to now was not investigated using neural networks. ...
In this paper we present a novel deep framework for a watermarking - a technique of embedding a transparent message into an image in a way that allows retrieving the message from a (perturbed) copy, so ...
frequency approach, combined with Singular Value Decomposition (SVD) was proposed in [5, 6] (sharp frequency localized contourlet transform and discrete wave transform were used respectively) as frequency-domain ...
arXiv:2006.03921v1
fatcat:7hfv5ujzcffariy5d4b2cxw2ly
Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM
2021
International Journal of Interactive Multimedia and Artificial Intelligence
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After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). ...
Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. ...
Digital images must be protected because they have high-value added contents for intellectual property rights. ...
doi:10.9781/ijimai.2021.10.006
fatcat:dwr6zwpevbfszmeefhajh6c23m
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
[article]
2021
arXiv
pre-print
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This survey summarises recent developments in deep learning techniques for data hiding for the purposes of watermarking and steganography, categorising them based on model architectures and noise injection ...
Digital watermarking is a form of data hiding where identifying data is robustly embedded so that it can resist tampering and be used to identify the original owners of the media. ...
Therefore, there is a growing need to protect machine learning models as intellectual property. ...
arXiv:2107.09287v1
fatcat:2sqcyzv6t5ccdiffk5cmag7tya
ReDMark: Framework for Residual Diffusion Watermarking on Deep Networks
[article]
2018
arXiv
pre-print
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The framework is composed of two Fully Convolutional Neural Networks with the residual structure for embedding and extraction. ...
Due to the rapid growth of machine learning tools and specifically deep networks in various computer vision and image processing areas, application of Convolutional Neural Networks for watermarking have ...
Since then it has been applied for identification of image ownership and protection of intellectual property by hiding data such as logos and proprietary information in images, videos and audios [2] . ...
arXiv:1810.07248v3
fatcat:nm5ozkz7abgy3kteuerdc4x6ou
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