Fault tolerance of neural networks in adversarial settings

Duddu, Vasisht; Pillai, N. Rajesh; Rao, Dushyant; Balas, Valentina E.

doi:10.3233/jifs-179677

Cited by 13 publications

(5 citation statements)

References 13 publications

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“…This ensures that model is free from over fitting and it can be concluded that designed CNN model got ability of fault tolerance. Further, the proposed model has local connectivity, shared weights, and spatial pooling which ensures that fault features will not processed and model got good at fault diagnosis [53].…”

Section: Results Analysismentioning

confidence: 99%

Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Vaddi

Manoharan

2020

IET Image Processing

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Hyperspectral image (HSI) consists of hundreds of contiguous spectral bands, which can be used in the classification of different objects on the earth. The inclusion of both spectral as well as spatial features stands essential in order that high classification accuracy is achieved. However, incorporation of the spectral and spatial information without preserving the intrinsic structure of the data leads on to downscaling the classification accuracy. To address the issue aforementioned, the proposed method which involves using unsupervised spectral band selection based on three major constrains: (i) low reconstruction error with neighbourhood bands, (ii) low noise, (iii) high information entropy, is put forward. In addition, the structure-preserving recursive filter is used to extract spatial features. Finally, the classification is performed using convolutional neural networks (CNNs) with different sets of convolutional, pooling, and fully connected layers. To test the performance of the proposed method, experiments have been carried out with three benchmark HSI datasets Indian pines, University of Pavia, and Salinas. These experiments reveal that the proposed method offers better classification accuracy over the purportedly state-of-the-art methods in terms of standard metrics like overall accuracy, average accuracy, and kappa coefficient (K). The proposed method has attained OAs of 99.9, 98.9, and 99.93% for the three datasets, respectively.

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Section: Results Analysismentioning

confidence: 99%

Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Vaddi

Manoharan

2020

IET Image Processing

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“…This adversary uses a regressor, trained using different execution times along with the respective number of layers in the network. This information is then utilized to mimic substitute models with similar functionalities to the original network 63 . Information about the CNN model can be leaked using reverse engineering of structure and weights with the help of memory and timing side‐channel attacks.…”

Section: The Attack Surface Of Artificial Intelligencementioning

confidence: 99%

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Shinde,

Patil,

Kotecha

et al. 2023

Trans Emerging Tel Tech

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Healthcare institutions are progressively integrating artificial intelligence (AI) into their operations. The extraordinary potential of AI is restricted by insufficient medical data for AI model training and adversarial attacks wherein attackers perturb the dataset by adding some noise to it, which leads to the malfunctioning of the AI models, and a lack of trust caused by the opaque operational approach it employs. This Systematic Literature Review (SLR) is a state‐of‐the‐art survey of the research on blockchain technology for securing AI‐integrated healthcare applications. The most relevant articles from the Scopus and Web of Science (WoS) databases were identified using the PRISMA model. Most of the existing literature is about protecting the healthcare data used by AI‐based healthcare systems using blockchain technology, but the modality of data (text, images, audio, and sound) was not specifically mentioned. Information on protecting the training phase and model deployment for AI‐based healthcare systems considering the variations in feature extraction based on the modality of data was also not clearly specified. Hence, the three subfields of AI, namely, natural language processing (NLP), computer vision, and acoustic AI are further studied to identify security loopholes in its implementation pipeline. The three phases, namely the dataset, the training phase, and the trained models need to be protected from adversaries to avoid malfunctioning of the deployed AI models. The nature of the data processed by NLP, computer vision, and acoustic AI, underlying deep neural network (DNN) architectures, the complexity of attacks, and the perceivability of attacks by humans are analyzed to identify the need for security. A blockchain solution for AI‐based healthcare systems is synthesized based on the findings that have demonstrated the distinctive technological features of blockchains. It offers a solution for the privacy and security issues encountered by NLP, computer vision, and acoustic AI to boost the widespread adoption of AI applications in healthcare.

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“…ey concluded that ensemble predictors may improve performance of fault detection to some degree. Duddu et al [24], in their work, considered the trade-off between adversarial robustness, fault tolerance, and privacy. Two adversarial settings were also considered under the security and privacy threat model.…”

Section: Related Workmentioning

confidence: 99%

Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults

Gupta

Rajnish

Bhattacherjee

2021

Scientific Programming

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Deep neural network models built by the appropriate design decisions are crucial to obtain the desired classifier performance. This is especially desired when predicting fault proneness of software modules. When correctly identified, this could help in reducing the testing cost by directing the efforts more towards the modules identified to be fault prone. To be able to build an efficient deep neural network model, it is important that the parameters such as number of hidden layers, number of nodes in each layer, and training details such as learning rate and regularization methods be investigated in detail. The objective of this paper is to show the importance of hyperparameter tuning in developing efficient deep neural network models for predicting fault proneness of software modules and to compare the results with other machine learning algorithms. It is shown that the proposed model outperforms the other algorithms in most cases.

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Fault tolerance of neural networks in adversarial settings

Cited by 13 publications

References 13 publications

Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Securing AI‐based healthcare systems using blockchain technology: A state‐of‐the‐art systematic literature review and future research directions

Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults

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