scholarly journals Cloud-Based Fusion of Residual Exploitation-Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Amit Doegar ◽  
Srinidhi Hiriyannaiah ◽  
G. M. Siddesh ◽  
K. G. Srinivasa ◽  
Maitreyee Dutta
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Research Work ◽  
Network Models ◽  
Neural Network Models ◽  
Tampering Detection ◽  
Image Tampering ◽  
Efficient Detection ◽  
Image Tampering Detection ◽  
Learning Architectures

Cloud computing has evolved in various application areas such as medical imaging and bioinformatics. It raises the issues of privacy and tampering in the images especially related to the medical field and bioinformatics for various reasons. The digital images are quite vulnerable to be tampered by the interceptors. The credibility of individuals can transform through falsified information in the images. Image tampering detection is an approach to identifying and finding the tampered components in the image. For the efficient detection of image tampering, the sufficient number of features are required which can be achieved by a deep learning architecture-based models without manual feature extraction of functions. In this research work, we have presented and implemented a cloud-based residual exploitation-based deep learning architectures to detect whether or not an image is being tampered. The proposed approach is implemented on the publicly available benchmark MICC-F220 dataset with the k -fold cross-validation approach to avoid the overfitting problem and to evaluate the performance metrics.

A Deep Learning-Based Method for Image Tampering Detection

2021 ◽  
pp. 170-184
Author(s):  
Kha-Tu Huynh ◽  
Tu-Nga Ly ◽  
Thuong Le-Tien
Keyword(s):  
Deep Learning ◽  
Tampering Detection ◽  
Image Tampering ◽  
Image Tampering Detection

Image Tampering Detection Using Convolutional Neural Network

2019 ◽  
Vol 10 (1) ◽  
pp. 54-63
Author(s):  
Shruti Singhania ◽  
Arju N.A ◽  
Raina Singh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Rapid Growth ◽  
Detection Method ◽  
Research Work ◽  
Optimal Number ◽  
Tampering Detection ◽  
Image Tampering ◽  
Image Tampering Detection ◽  
Journalism Research

Pictures are considered the most reliable form of media in journalism, research work, investigations, and intelligence reporting. With the rapid growth of ever-advancing technology and free applications on smartphones, sharing and transferring images is widely spread, which requires authentication and reliability. Copy-move forgery is considered a common image tampering type, where a part of the image is superimposed with another image. Such a tampering process occurs without leaving any obvious visual traces. In this study, an image tampering detection method was proposed by exploiting a convolutional neural network (CNN) for extracting the discriminative features from images and detects whether an image has been forged or not. The results established that the optimal number of epochs is 50 epochs using AlexNet-based CNN for classification-based tampering detection, with a 91% accuracy.

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

Feature extraction-based image steganalysis using deep learning

2021 ◽  
pp. 188-198
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Secure Communication ◽  
Information Technologies ◽  
Network Models ◽  
Error Rates ◽  
Multimedia Data ◽  
Secret Message ◽  
Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

scholarly journals Deep Network Guided Proof Search

10.29007/8mwc ◽  
2018 ◽  
Author(s):  
Sarah Loos ◽  
Geoffrey Irving ◽  
Christian Szegedy ◽  
Cezary Kaliszyk
Keyword(s):  
Deep Learning ◽  
Program Analysis ◽  
Network Models ◽  
Theorem Prover ◽  
Neural Network Models ◽  
Two Phase ◽  
First Order ◽  
Proof Search ◽  
System Verification ◽  
Theory Reasoning

Deep learning techniques lie at the heart of several significant AI advances in recent years including object recognition and detection, image captioning, machine translation, speech recognition and synthesis, and playing the game of Go.Automated first-order theorem provers can aid in the formalization and verification of mathematical theorems and play a crucial role in program analysis, theory reasoning, security, interpolation, and system verification.Here we suggest deep learning based guidance in the proof search of the theorem prover E. We train and compare several deep neural network models on the traces of existing ATP proofs of Mizar statements and use them to select processed clauses during proof search. We give experimental evidence that with a hybrid, two-phase approach, deep learning based guidance can significantly reduce the average number of proof search steps while increasing the number of theorems proved.Using a few proof guidance strategies that leverage deep neural networks, we have found first-order proofs of 7.36% of the first-order logic translations of the Mizar Mathematical Library theorems that did not previously have ATP generated proofs. This increases the ratio of statements in the corpus with ATP generated proofs from 56% to 59%.

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