A comprehensive review on GANs for time-series signals

Zhang, Da; Ma, Ming; Likun, Xia

doi:10.1007/s00521-022-06888-0

Cited by 22 publications

(13 citation statements)

References 73 publications

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“…5 shows the optimum cover JPEG image of the estimate approach based on sub-image cover probability. In theory, it is possible to compute the probability of all possible coverings and look for an equation (11). In contrast, the cover image has too many coefficient values to explore it thoroughly.…”

Section: Loss Functionmentioning

confidence: 99%

“…Co-frequency sub-image estimates for T r e should be calculated using the cover co-frequency sub-image estimation D r e with the highest posterior probability q, in terms of statistical inference  D r e is given in (11):…”

Section: Image Co-frequency Markov Model (Mm)mentioning

confidence: 99%

“…LSB and LSB Matching (LSBM) are notable examples of this work [10]. As a result, adaptive steganography employs various techniques to hide the hidden message [11].…”

Section: Introductionmentioning

confidence: 99%

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Identifying optimal message embedding location in audio steganography using generative adversarial networks

Hajer¹,

Anbar

2022

EEJET

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Audio steganography (AS) uses the auditory redundancy of the human ear to conceal the hidden message inside the audio track. In recent studies, deep learning-based steganalysis has swiftly revealed AS by extracting high-dimensional stego acoustic features for categorization. There is still an opportunity for improvement in the current audio steganography required for managing communication confidentiality, access control and data protection. The main objective of this research is to improving the data protection by identifying the data embedding location in the audio. Generative Adversarial Network-based Audio Steganography Framework (GAN-ASF) is presented in this study, and it can automatically learn to provide better cover audio for message embedding. The suggested framework's training architecture comprises a generator, a discriminator, and a steganalyzer learned using deep learning. The Least Significant Bit Matching (LSBM) message embedding technique encrypts the secret message into the steganographic cover audio, which is then forwarded to a trained steganalyzer for misinterpretation as cover audio. After performing the training, stenographic cover audio has been generated for encoding the secret message. Here, Markov model of co-frequency sub images to generate the best cover frequency sub-image to locate an image's hidden payload. Steganographic cover audio created by GAN-ASF has been tested and found to be of excellent quality for embedding messages. The suggested method's detection accuracy is lower than that of the most current state-of-the-art deep learning-based steganalysis. This payload placement approach has considerably increased stego locations' accuracy in low frequencies. The test results GAN-ASF achieves a performance ratio of 94.5 %, accuracy ratio of 96.2 %, an error rate of 15.7 %, SNR 24.3 %, and an efficiency ratio of 94.8 % compared to other methods.

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Section: Loss Functionmentioning

confidence: 99%

Section: Image Co-frequency Markov Model (Mm)mentioning

confidence: 99%

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Identifying optimal message embedding location in audio steganography using generative adversarial networks

Hajer¹,

Anbar

2022

EEJET

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“…The unsupervised learning method of GANs is finding widespread use, particularly in the realms of picture generation and data augmentation, and has just recently entered the public consciousness [7]. One-dimensional GANs have just begun to emerge from their infancy and it uses for time-series signals [8]. In order to pave the way for future study into the application of GANs for this purpose, this paper presents the use of GANs-based in radar signal creation, especially LFM signal generation.…”

Section: Introductionmentioning

confidence: 99%

Generating radar signals using one-dimensional GAN-based model for target classification in radar systems

Abdelfattah,

Ahmed,

Maher

et al. 2023

J. Phys.: Conf. Ser.

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Conventional radar systems are often unable to produce highly accurate results for target classification and identification via linear frequency modulation (LFM) signals. The potential of artificial intelligence, particularly deep learning, has been applied in various fields, which promotes utilizing them in the context of target classification in radar systems. However, to train deep learning models for this task, large datasets of LFM radar signals are required, which are practically difficult to obtain due to the time, effort, and involved high cost. Therefore, the presented work spots the light on utilizing the recent one-dimensional generative adversarial network (GAN) and Wasserstein GAN (WGAN) models to synthesize a large time-series LFM signal dataset from a reference smaller one. Moreover, the work fairly judges the generated LFM signals realistic via a decent qualitative and quantitative analysis, unlike other studies which rely solely on qualitative evaluation by human observers. The proposed study outcome reveals the WGAN’s efficiency in synthesizing high-quality LFM signals while reducing the training time and resource requirements.

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“…It is at this point that our work starts by solving the problem of the limited amount of training data using a generative adversarial network (GAN) [11].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals

et al. 2022

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This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCAD™). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate.

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A comprehensive review on GANs for time-series signals

Cited by 22 publications

References 73 publications

Identifying optimal message embedding location in audio steganography using generative adversarial networks

Identifying optimal message embedding location in audio steganography using generative adversarial networks

Generating radar signals using one-dimensional GAN-based model for target classification in radar systems

Detection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals

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