SSIMLayer: Towards robust deep representation learning via nonlinear structural similarity
Version 2 2024-06-04, 02:22Version 2 2024-06-04, 02:22
Version 1 2020-01-21, 01:54Version 1 2020-01-21, 01:54
conference contribution
posted on 2024-06-04, 02:22 authored by A Abobakr, M Hossny, S Nahavandi© 2019 IEEE. Adversarial examples form a major threat to incorporating machine learning (ML) models in critical applications. The existence and generalisation of these attacks have been attributed to the linear nature of ML models, deep neural network models in particular, in the high dimensional space. This paper presents a new nonlinear computational layer to the deep convolutional neural network architectures. This layer performs a set of comprehensive convolution operations that mimics the overall function of the human visual system (HVS) via focusing on learning structural information. The core of its computations is evaluating the components of the structural similarity metric (SSIM) in a setting that allows the kernels to learn to match structural information. The proposed SSIMLayer is inherently nonlinear. Experiments conducted on CIFAR-10 benchmark demonstrate that the SSIMLayer provides high learning capacity and shows more robustness against adversarial attacks.
History
Pagination
1234-1238Location
Bari, ItalyPublisher DOI
Start date
2019-10-06End date
2019-10-09ISSN
1062-922XISBN-13
9781728145693Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
SMC 2019 : Proceedings of the IEEE International Conference on Systems, Man and CyberneticsEvent
Systems, Man and Cybernetics. Conference (2019 : Bari, Italy)Publisher
IEEEPlace of publication
Piscataway, N.J.Usage metrics
Keywords
Licence
Exports
RefWorksRefWorks
BibTeXBibTeX
Ref. managerRef. manager
EndnoteEndnote
DataCiteDataCite
NLMNLM
DCDC