scholarly journals DIGDH: A Novel Framework of Difference Image Grafting Deep Hiding for Image Data Hiding

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 151
Author(s):  
Xintao Duan ◽  
Lei Li ◽  
Yao Su ◽  
Wenxin Wang ◽  
En Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Hiding ◽  
Deep Neural Networks ◽  
Random Distribution ◽  
Image Data ◽  
Experimental Results ◽  
The Real ◽  
Difference Image ◽  
Audio Media

Data hiding is the technique of embedding data into video or audio media. With the development of deep neural networks (DNN), the quality of images generated by novel data hiding methods based on DNN is getting better. However, there is still room for the similarity between the original images and the images generated by the DNN models which were trained based on the existing hiding frameworks to improve, and it is hard for the receiver to distinguish whether the container image is from the real sender. We propose a framework by introducing a key_img for using the over-fitting characteristic of DNN and combined with difference image grafting symmetrically, named difference image grafting deep hiding (DIGDH). The key_img can be used to identify whether the container image is from the real sender easily. The experimental results show that without changing the structures of networks, the models trained based on the proposed framework can generate images with higher similarity to original cover and secret images. According to the analysis results of the steganalysis tool named StegExpose, the container images generated by the hiding model trained based on the proposed framework is closer to the random distribution.

scholarly journals Learning Neural Audio Embeddings for Grounding Semantics in Auditory Perception

2017 ◽  
Vol 60 ◽  
pp. 1003-1030 ◽  
Author(s):  
Douwe Kiela ◽  
Stephen Clark
Keyword(s):  
Neural Networks ◽  
Auditory Perception ◽  
Deep Neural Networks ◽  
Image Data ◽  
Auditory Information ◽  
Semantic Representations ◽  
Modal Semantics ◽  
Feature Norms ◽  
Auditory Data

Multi-modal semantics, which aims to ground semantic representations in perception, has relied on feature norms or raw image data for perceptual input. In this paper we examine grounding semantic representations in raw auditory data, using standard evaluations for multi-modal semantics. After having shown the quality of such auditorily grounded representations, we show how they can be applied to tasks where auditory perception is relevant, including two unsupervised categorization experiments, and provide further analysis. We find that features transfered from deep neural networks outperform bag of audio words approaches. To our knowledge, this is the first work to construct multi-modal models from a combination of textual information and auditory information extracted from deep neural networks, and the first work to evaluate the performance of tri-modal (textual, visual and auditory) semantic models.

Robust image data hiding method based on multiple backups and pixel bit weight

2021 ◽  
pp. 1-14
Author(s):  
Yu Dong ◽  
Xianquan Zhang ◽  
Chunqiang Yu ◽  
Zhenjun Tang ◽  
Guoen Xia
Keyword(s):  
Data Hiding ◽  
Digital Images ◽  
Image Data ◽  
Experimental Results ◽  
Cover Image ◽  
Secret Image ◽  
Robust Image ◽  
Reference Matrix ◽  
Least Significant Bits

Digital images are easily corrupted by attacks during transmission and most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, we propose a robust image data hiding method based on multiple backups and pixel bit weight (PBW). Especially multiple backups of every pixel bit are pre-embedded into a cover image according to a reference matrix. Since different pixel bits have different weights, the most significant bits (MSBs) occupy more weights on the secret image than those of the least significant bits (LSBs). Accordingly, some backups of LSBs are substituted by the MSBs to increase the backups of MSBs so that the quality of the extracted secret image can be improved. Experimental results show that the proposed algorithm is robust to cropping and noise attacks for secret image.

scholarly journals Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Image Data ◽  
Fine Tuning ◽  
Cell Detection ◽  
Imaging Protocol ◽  
Microscopy Image ◽  
Microscopy Images ◽  
Target Data

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

scholarly journals Label Distribution for Learning with Noisy Labels

2020 ◽  
Author(s):  
Yun-Peng Liu ◽  
Ning Xu ◽  
Yu Zhang ◽  
Xin Geng
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Confidence Estimation ◽  
Novel Method ◽  
Real World Datasets ◽  
Label Distribution ◽  
Noisy Labels

The performances of deep neural networks (DNNs) crucially rely on the quality of labeling. In some situations, labels are easily corrupted, and therefore some labels become noisy labels. Thus, designing algorithms that deal with noisy labels is of great importance for learning robust DNNs. However, it is difficult to distinguish between clean labels and noisy labels, which becomes the bottleneck of many methods. To address the problem, this paper proposes a novel method named Label Distribution based Confidence Estimation (LDCE). LDCE estimates the confidence of the observed labels based on label distribution. Then, the boundary between clean labels and noisy labels becomes clear according to confidence scores. To verify the effectiveness of the method, LDCE is combined with the existing learning algorithm to train robust DNNs. Experiments on both synthetic and real-world datasets substantiate the superiority of the proposed algorithm against state-of-the-art methods.

scholarly journals Data hiding using bit plane complexity segmentation steganography

2018 ◽  
Vol 7 (2.20) ◽  
pp. 33
Author(s):  
M Chandra Sekhar ◽  
S K. Chandini ◽  
V Sai Rohith ◽  
V Jhansi Lakshmi ◽  
M Pavan Kumar
Keyword(s):  
Data Hiding ◽  
Image Data ◽  
Limited Data ◽  
Information Measures ◽  
Bit Plane ◽  
Bit Planes ◽  
Human Attributes ◽  
Cover Up

Steganography is the basis of information covering the puzzle in some other data (we call it the ship), leaving no obvious evidence of data change. Most conventional steganographic strategies is limited data that hide the limit. They can cover up just 10% (or less) of the information measures of the vessel. This is on the grounds that the standard of those procedures was either to supplant an uncommon piece of the recurrence parts of the vessel picture, or to supplant all the slightest critical image bits with a secret multi-valued data. Our new Steganography uses the image as vessel information, and we enter the data in the bit-plane mystery vessel. This strategy makes use of human attributes, through which people cannot see the structure of any form of data exclusively damned pair, for example. We can replace most of the «Commotion like" regions in the bit-planes of the puzzle vessel image data without deteriorating the quality of the photos. We called our steganography "BPCS-steganography," which remains a Bit-Plane Complexity Segmentation steganography.  

scholarly journals Automated and Manual Quantification of Tumour Cellularity in Digital Slides for Tumour Burden Assessment

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Shazia Akbar ◽  
Mohammad Peikari ◽  
Sherine Salama ◽  
Azadeh Yazdan Panah ◽  
Sharon Nofech-Mozes ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Traditional Approach ◽  
Quantitative Measure ◽  
Image Data ◽  
Qualitative Assessment ◽  
Residual Cancer Burden ◽  
Automated Algorithms ◽  
Tumour Cellularity

Abstract The residual cancer burden index is an important quantitative measure used for assessing treatment response following neoadjuvant therapy for breast cancer. It has shown to be predictive of overall survival and is composed of two key metrics: qualitative assessment of lymph nodes and the percentage of invasive or in situ tumour cellularity (TC) in the tumour bed (TB). Currently, TC is assessed through eye-balling of routine histopathology slides estimating the proportion of tumour cells within the TB. With the advances in production of digitized slides and increasing availability of slide scanners in pathology laboratories, there is potential to measure TC using automated algorithms with greater precision and accuracy. We describe two methods for automated TC scoring: 1) a traditional approach to image analysis development whereby we mimic the pathologists’ workflow, and 2) a recent development in artificial intelligence in which features are learned automatically in deep neural networks using image data alone. We show strong agreements between automated and manual analysis of digital slides. Agreements between our trained deep neural networks and experts in this study (0.82) approach the inter-rater agreements between pathologists (0.89). We also reveal properties that are captured when we apply deep neural network to whole slide images, and discuss the potential of using such visualisations to improve upon TC assessment in the future.

Enhanced Fusion of Deep Neural Networks for Classification of Benchmark High-Resolution Image Data Sets

2018 ◽  
Vol 15 (9) ◽  
pp. 1451-1455 ◽  
Author(s):  
Grant J. Scott ◽  
Kyle C. Hagan ◽  
Richard A. Marcum ◽  
James Alex Hurt ◽  
Derek T. Anderson ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Deep Neural Networks ◽  
Image Data ◽  
Data Sets ◽  
Resolution Image ◽  
High Resolution Image

scholarly journals A HYBRID MODEL USING THE PRETRAINED BERT AND DEEP NEURAL NETWORKS WITH RICH FEATURE FOR EXTRACTIVE TEXT SUMMARIZATION

2021 ◽  
Vol 37 (2) ◽  
pp. 123-143
Author(s):  
Tuan Minh Luu ◽  
Huong Thanh Le ◽  
Tan Minh Hoang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Text Summarization ◽  
Training Dataset ◽  
Extractive Summarization ◽  
Input Text ◽  
Summarization System ◽  
Fully Connected

Deep neural networks have been applied successfully to extractive text summarization tasks with the accompany of large training datasets. However, when the training dataset is not large enough, these models reveal certain limitations that affect the quality of the system’s summary. In this paper, we propose an extractive summarization system basing on a Convolutional Neural Network and a Fully Connected network for sentence selection. The pretrained BERT multilingual model is used to generate embeddings vectors from the input text. These vectors are combined with TF-IDF values to produce the input of the text summarization system. Redundant sentences from the output summary are eliminated by the Maximal Marginal Relevance method. Our system is evaluated with both English and Vietnamese languages using CNN and Baomoi datasets, respectively. Experimental results show that our system achieves better results comparing to existing works using the same dataset. It confirms that our approach can be effectively applied to summarize both English and Vietnamese languages.

scholarly journals Regularizing Deep Neural Networks with an Ensemble-based Decorrelation Method

2018 ◽  
Author(s):  
Shuqin Gu ◽  
Yuexian Hou ◽  
Lipeng Zhang ◽  
Yazhou Zhang
Keyword(s):  
Neural Networks ◽  
Ensemble Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Experimental Results ◽  
Excellent Performance ◽  
Hidden Layer ◽  
Base Learner ◽  
Fully Connected

Although Deep Neural Networks (DNNs) have achieved excellent performance in many tasks, improving the generalization capacity of DNNs still remains a challenge. In this work, we propose a novel regularizer named Ensemble-based Decorrelation Method (EDM), which is motivated by the idea of the ensemble learning to improve generalization capacity of DNNs. EDM can be applied to hidden layers in fully connected neural networks or convolutional neural networks. We treat each hidden layer as an ensemble of several base learners through dividing all the hidden units into several non-overlap groups, and each group will be viewed as a base learner. EDM encourages DNNs to learn more diverse representations by minimizing the covariance between all base learners during the training step. Experimental results on MNIST and CIFAR datasets demonstrate that EDM can effectively reduce the overfitting and improve the generalization capacity of DNNs  

scholarly journals How Many Features is an Image Worth? Multi-Channel CNN for Steering Angle Prediction in Autonomous Vehicles

2021 ◽  
Author(s):  
Jason Munger ◽  
Carlos W. Morato
Keyword(s):  
Neural Networks ◽  
Autonomous Vehicles ◽  
Deep Neural Networks ◽  
Spatial Information ◽  
Image Data ◽  
Input Image ◽  
Steering Angle ◽  
Rgb Images ◽  
And Performance ◽  
The Individual

This project explores how raw image data obtained from AV cameras can provide a model with more spatial information than can be learned from simple RGB images alone. This paper leverages the advances of deep neural networks to demonstrate steering angle predictions of autonomous vehicles through an end-to-end multi-channel CNN model using only the image data provided from an onboard camera. Image data is processed through existing neural networks to provide pixel segmentation and depth estimates and input to a new neural network along with the raw input image to provide enhanced feature signals from the environment. Various input combinations of Multi-Channel CNNs are evaluated, and their effectiveness is compared to single CNN networks using the individual data inputs. The model with the most accurate steering predictions is identified and performance compared to previous neural networks.

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