Conditional Generative Adversarial Networks with Adversarial Attack and Defense for Generative Data Augmentation

Baek, Francis; Kim, Daeho; Park, Somin; Kim, Hyoungkwan; Sang-Hyun, Lee

doi:10.1061/(asce)cp.1943-5487.0001015

Cited by 19 publications

(3 citation statements)

References 56 publications

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“…Upon extension, D and G networks are conditioned on additional data (y) to overcome reliance on random variables in the original model [353]. y denotes auxiliary data from other modalities or class labels.…”

Section: Conditional Gans (Cgan)mentioning

confidence: 99%

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A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

et al. 2023

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Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

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Section: Conditional Gans (Cgan)mentioning

confidence: 99%

“…12). In the G network, prior input noise pz(z) and y are integrated in joint hidden representation, while the adversarial training framework permits considerable flexibility in the composition of this hidden representation [353]. In the D network, both x and y are presented as inputs to a D function.…”

Section: Conditional Gans (Cgan)mentioning

confidence: 99%

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

et al. 2023

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“…Enhancing defect datasets is a key method for improving the accuracy of defect detection. Utilizing generative adversarial networks (GANs) for data augmentation is a useful and efficient technique, and this approach has been applied in various domains, including architecture, 24 medicine, 25,26 facial recognition, 27 fabrics, 28,29 and more. Traditional GANs used in image generation face challenges such as model collapse, gradient vanishing, and gradient explosion.…”

mentioning

confidence: 99%

Fabric defect image generation method based on the dual-stage W-net generative adversarial network

Hu,

Liang,

Wang

et al. 2024

Textile Research Journal

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Due to the intricate and diverse nature of textile defects, detecting them poses an exceptionally challenging task. In comparison with conventional defect detection methods, deep learning-based defect detection methods generally exhibit superior precision. However, utilizing deep learning for defect detection requires a substantial volume of training data, which can be particularly challenging to accumulate for textile flaws. To augment the fabric defect dataset and enhance fabric defect detection accuracy, we propose a fabric defect image generation method based on Pix2Pix generative adversarial network. This approach devises a novel dual-stage W-net generative adversarial network. By increasing the network depth, this model can effectively extract intricate textile image features, thereby enhancing its ability to expand information sharing capacity. The dual-stage W-net generative adversarial network allows generating desired defects on defect-free textile images. We conduct quality assessment of the generated fabric defect images resulting in peak signal-to-noise ratio and structural similarity values exceeding 30 and 0.930, respectively, and a learned perceptual image patch similarity value no greater than 0.085, demonstrating the effectiveness of fabric defect data augmentation. The effectiveness of dual-stage W-net generative adversarial network is established through multiple comparative experiments evaluating the generated images. By examining the detection performance before and after data augmentation, the results demonstrate that mean average precision improves by 6.13% and 14.57% on YOLO V5 and faster recurrent convolutional neural networks detection models, respectively.

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A lightweight security defense algorithm for multimodal attack blocking in industrial control networks based on momentum enhanced feature graphs

Yang,

Gao,

Wang

et al. 2024

Security and Privacy

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As the infrastructure of modern industry, the safe and stable operation of lightweight industrial control network is crucial for industrial production. Multi‐mode attack is a kind of highly customized and low resource consumption network attack method, which can target the specific vulnerabilities and configuration weaknesses of lightweight industrial control network. In order to improve the defense capability of industrial control network against multimodal attacks, a lightweight security defense algorithm based on momentum‐enhanced feature graph is proposed. The lightweight industrial control network operation mode is determined by abstract relationship, and the ontology structure of industrial control network multi‐mode attack is established; with reference to the ontology structure, the multi‐mode attack map of industrial control network is defined; the normal behavior and lightweight attack behavior are divided, and the convolutional network is used to enhance the characteristics of lightweight attack; and the momentum‐enhanced feature map technology is used to correspond to the enhancement process, and to block and defend against the multi‐mode attack of industrial control network. The results show that the studied algorithm can realize accurate prediction of attack behaviors with different attack behaviors as test objects, and ensure accurate blocking of all kinds of attack behaviors with better security defense effect.

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Conditional Generative Adversarial Networks with Adversarial Attack and Defense for Generative Data Augmentation

Cited by 19 publications

References 56 publications

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications

Fabric defect image generation method based on the dual-stage W-net generative adversarial network

A lightweight security defense algorithm for multimodal attack blocking in industrial control networks based on momentum enhanced feature graphs

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