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A Comprehensive Study of Deep Learning Based Covert Communication
2022
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Further, major role of deep learning in the area of information hiding are highlighted. ...
Deep learning-based methods have been popular in multimedia analysis tasks, including classification, detection, segmentation, and so on. ...
Deep neural network (DNN), convolutional neural network (CNN) and recurrent neural network (RNN) are some of the commonly used deep learning models. ...
doi:10.1145/3508365
fatcat:kboo4h4gn5gahd6yimd3i4d5my
A Secure Image Watermarking Architecture based on DWT-DCT Domain and Pseudo-Random Number
2019
International journal of recent technology and engineering
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Protection against tapering the media contents is one of the challenges facing the individuals and industry in a digital age, in another hand rise of artificial neural network models such as deep fake ...
In this method, we used four random number as seeds generated by a pseudo-random number generator, randomised numbers used as Initial value for encryption of watermark image. ...
They create PRN using Elman neural network, then decompose the image into domain wavelet and watermark the cover image using the PRN sequence generated by the neural network. ...
doi:10.35940/ijrte.d8724.118419
fatcat:fmj2uounwva7bjkoaacjwv5jii
Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations
[article]
2021
arXiv
pre-print
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To deal with such security threats, a few deep neural networks (DNN) IP protection methods have been proposed in recent years. ...
The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. ...
Index Terms-Deep neural networks, intellectual property protection, machine learning security, taxonomy, attack resistance
I. ...
arXiv:2011.13564v2
fatcat:lbts5q52b5axzlmuitb2u62dfa
CropDefender: deep watermark which is more convenient to train and more robust against cropping
[article]
2021
arXiv
pre-print
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In recent years, some research has proposed the use of neural networks to add watermarks to natural images. We take StegaStamp as an example for our research. ...
By explicitly introducing the perturbation of cropping into the training, the cropping resistance is significantly improved. ...
In our experiments, we found that the neural-network-based watermark generation technique has amazing robustness. ...
arXiv:2109.06651v1
fatcat:gqpocwzcr5hejoo6mlgspk7poy
Framework for Improvising Dual Digital Watermarking through Deep Learning Techniques
2018
International Journal of Engineering Research and
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The papers also propose a framework with Deep learning techniques to improvise the existing systems. ...
Achieving better compression rates in dual digital watermarking is still area of concern. Digital watermarking has four techniques for compression the image. ...
III FRAMEWORK MODEL The proposed system implements Convolutional Neural Network(CNN) is the deep class that is it is implemented by Forward Neural Networks(FNN) which is used to analysis the visual image ...
doi:10.17577/ijertv7is020138
fatcat:bfqj7rm3k5hlje5ibtqhnlw6di
Detecting Digital Watermarking Image Attacks Using a Convolution Neural Network Approach
2022
Security and Communication Networks
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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 ...
, average filter, motion blur, and no attack, to improve the watermarking quality. ...
Artificial neural networks (ANNs) with several layers are referred to as "deep learning" or "deep neural networks." ...
doi:10.1155/2022/4408336
fatcat:nicpyvlzqzczhknvesjp2epzmm
Special issue on real-time image watermarking and forensics in cloud computing
2019
Journal of Real-Time Image Processing
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Another paper entitled "Deep Neural Networks for Efficient Steganographic Payload Location", co-authored by Yu Sun, Hao Zhang, Tao Zhang, and Ran Wang, presents a tailored deep neural network (DNN) with ...
Image forensics The paper entitled "A Multi-purpose Image Forensic Method Using Densely Connected Convolutional Neural Networks", co-authored by Yifang Chen, Xiangui Kang, Yun-Qing Shi, and Z. ...
doi:10.1007/s11554-019-00881-y
fatcat:qgkqy5fxxbhfhj3l4ptrmcoewy
An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks
[article]
2020
arXiv
pre-print
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In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. ...
To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. ...
Preparation of Datasets The proposed deep learning-based image watermarking architecture was trained as a single deep neural network. ...
arXiv:2007.02460v1
fatcat:5bd4a3xmjvge7he6wv5cv2jlce
Guest editorial: Recent trends in multimedia data-hiding: a reliable mean for secure communications
2019
Journal of Ambient Intelligence and Humanized Computing
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"A study on user recognition using 2D ECG based on ensemble of deep convolutional neural networks" presents a user recognition method based on ensemble networks using ECG signals. ...
"Secret Image Sharing Scheme with Encrypted Shadow Images using Optimal Homomorphic Encryption Technique", a wavelet-based secret image sharing scheme using optimal Homomorphic Encryption is proposed. ...
doi:10.1007/s12652-019-01499-5
fatcat:2yletid6wngcnn2m5j2cunx6oe
Data hiding in encryption–compression domain
2021
Complex & Intelligent Systems
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Additionally, de-noising convolutional neural network is performed at extracted mark data to enhance the robustness of the scheme. ...
Then, we use appropriate scaling factor to invisibly embed the singular value of watermark data into the lower frequency sub-band of the host image. ...
Deep Learning toolbox is used to apply pre-trained denoising convolutional neural network. The several steps of De-noising process of recovered data are described in Algorithm 4. ...
doi:10.1007/s40747-021-00309-w
fatcat:ptywxlnf6jf75b55sgjmaeahka
Robust Black-box Watermarking for Deep NeuralNetwork using Inverse Document Frequency
[article]
2021
arXiv
pre-print
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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 ...
Deep learning techniques are one of the most significant elements of any Artificial Intelligence (AI) services. ...
The combination of deep learning and neural networks are called deep neural networks (DNNs). ...
arXiv:2103.05590v1
fatcat:esfm4plqjjgwjobal2nuwuh7be
Towards Practical Watermark for Deep Neural Networks in Federated Learning
[article]
2021
arXiv
pre-print
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With the wide application of deep neural networks, it is important to verify a host's possession over a deep neural network model and protect the model. ...
To meet those requirements, in this paper, we demonstrate a watermarking protocol for protecting deep neural networks in the setting of FL. ...
INTRODUCTION Deep neural networks (DNN) are intelligent systems that provide services by learning from data and consuming enormous computational resources. ...
arXiv:2105.03167v3
fatcat:j77q6sowmjagzejdqi676ulgme
A Robust Image Watermarking System Based on Deep Neural Networks
[article]
2019
arXiv
pre-print
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In this paper, a fully automated image watermarking system based on deep neural networks is proposed to generalize the image watermarking processes. ...
Incorporating deep learning networks with image watermarking has attracted increasing attention during recent years. ...
TABLE I COMPARISON I BETWEEN THE PROPOSED SYSTEM AND STATE-OF-THE-ART IMAGE WATERMARKING METHODS APPLYING DEEP NEURAL NETWORKS Method
Function of the
deep neural
network
Blind
Robust Concentration ...
arXiv:1908.11331v1
fatcat:vxwtyx32rvb57h74ffowri3i3q
Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication
2020
Proceedings of the 28th ACM International Conference on Multimedia
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2021 pre-print arXiv:2104.04268v1 fatcat:tkvzhxixhnh4fowtv6vtd2teb4
Deep convolutional neural networks have made outstanding contributions in many fields such as computer vision in the past few years and many researchers published well-trained network for downloading. ...
Specifically, we present the reversible watermarking problem of deep convolutional neural networks and utilize the pruning theory of model compression technology to construct a host sequence used for embedding ...
The backdoor is defined as a hidden pattern injected into a deep neural network model by modifying the parameters while training. ...
doi:10.1145/3394171.3413729
dblp:conf/mm/GuanFZZZY20
fatcat:5xw37ckspffp3ilb2cbgl4m36q
DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services
[article]
2020
arXiv
pre-print
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With the increasing artificial intelligence application, deep neural network (DNN) has become an emerging task. ...
Embedding a watermark takes 3 epochs and removing a watermark takes 30 epochs. ...
model, the miner will generate a watermark and embed the watermark into the deep neural network. ...
arXiv:1904.07349v2
fatcat:njbbs6plpzaapp3oe22p6ajgke
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