Photomontage detection using steganography technique based on a neural network

Jarušek, Robert; Volná, Eva; Kotyrba, Martin

doi:10.1016/j.neunet.2019.03.015

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…Data hiding image authentication methods are based on digital steganography [22,23,24] or digital watermarking [25,26,27]. Currently, research in both sub-areas is also exploring the use of neural networks [28,29,30].…”

Section: Authentication Of Images Based On Data Hidingmentioning

confidence: 99%

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

Forgáč,

Očkay,

Javurek

et al. 2023

cai

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This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work.

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Section: Authentication Of Images Based On Data Hidingmentioning

confidence: 99%

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

Forgáč,

Očkay,

Javurek

et al. 2023

cai

View full text Add to dashboard Cite

show abstract

“…In the BP neural network, the initial weights and thresholds need to be given after the network structure is determined. When the training data enters the input layer of the network, the weights and activation functions between the hidden layer and the input layer are used to calculate the actual output value after taking corresponding calculations [13], then the expected output value and the actual output value are used to calculate the actual error value. The error value in the actual situation is compared with the expected error value.…”

Section: Bp Neural Network Modelmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

Xiao

Liao

Bartoš

et al. 2021

Multimed Tools Appl

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In order to improve the accuracy of rolling bearing fault diagnosis in mechanical equipment, a new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed. This method use the global search ability of the cuckoo search algorithm to constantly search for the best weights and thresholds, and then give it to the back propagation neural network. In this paper, wavelet packet decomposition is used for feature extraction of vibration signals. The energy values of different frequency bands are obtained through wavelet packet decomposition, and they are input as feature vectors into optimized back propagation neural network to identify different fault types of rolling bearings. Through the three sets of simulation comparison experiments of Matlab, the experimental results show that, Under the same conditions, compared with the other five models, the proposed back propagation neural network optimized by cuckoo search algorithm has the least number of training iterations and the highest diagnostic accuracy rate. And in the complex classification experiment with the same fault location but different bearing diameters, the fault recognition correct rate of the back propagation neural network optimized by cuckoo search algorithm is 96.25%.

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“…In recent years, many scholars have conducted a series of studies on the intelligent diagnosis method of hydraulic system faults. Additionally, the BP neural network [22][23][24] and convolutional neural network [25,26] are popular among them. In order to make up for the shortcomings of previous research, the BP neural network optimization model and the convolutional neural network model are applied to the fault diagnosis of the HMCVT shift hydraulic system in this paper, and the classification results are compared.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN

Wang

et al. 2023

Agriculture

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There are some problems in the shifting process of hydraulic CVT, such as irregularity, low stability and high failure rate. In this paper, the BP neural network and convolutional neural network are used for fault diagnosis of the HMCVT hydraulic system. Firstly, through experiments, 120 groups of pressure and flow data under normal and four typical fault modes were obtained and preprocessed; they were divided into 80 groups of training samples and 40 groups of test samples via random extraction, using the BP neural network model and convolutional neural network model for fault classification. The results show that compared with BP, PSO-BP and other models, the fault diagnosis rate of the BAS-BP neural network model can reach 92.5%, and the average diagnosis accuracy rate of the convolutional neural network can reach 97.5%, which can be effectively applied to the fault diagnosis of the HMCVT hydraulic system and provide some reference for the shifting reliability of hydraulic CVT.

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Photomontage detection using steganography technique based on a neural network

Cited by 13 publications

References 20 publications

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

Fault diagnosis of rolling bearing based on back propagation neural network optimized by cuckoo search algorithm

Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN

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