A fast gradient-based algorithm for image reconstruction in inverse geometry CT architecture with sparse distributed sources

2019 ◽  
Author(s):  
Frédéric Jolivet ◽  
Clarisse Fournier ◽  
Andrea Brambilla
Keyword(s):  
Image Reconstruction ◽  
Gradient Based ◽  
Fast Gradient ◽  
Inverse Geometry

Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images

1994 ◽  
Vol 13 (4) ◽  
pp. 687-701 ◽  
Author(s):  
E.U. Mumcuoglu ◽  
R. Leahy ◽  
S.R. Cherry ◽  
Zhenyu Zhou
Keyword(s):  
Bayesian Reconstruction ◽  
Gradient Based ◽  
Fast Gradient

scholarly journals Random Transformation of image brightness for adversarial attack

2021 ◽  
pp. 1-12
Author(s):  
Bo Yang ◽  
Kaiyong Xu ◽  
Hengjun Wang ◽  
Hengwei Zhang
Keyword(s):  
Success Rate ◽  
Data Augmentation ◽  
Black Box ◽  
Image Brightness ◽  
Gradient Based ◽  
Adversarial Examples ◽  
Random Transformation ◽  
Fast Gradient ◽  
Adversarial Example ◽  
Sign Method

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding small, human-imperceptible perturbations to the original images, but make the model output inaccurate predictions. Before DNNs are deployed, adversarial attacks can thus be an important method to evaluate and select robust models in safety-critical applications. However, under the challenging black-box setting, the attack success rate, i.e., the transferability of adversarial examples, still needs to be improved. Based on image augmentation methods, this paper found that random transformation of image brightness can eliminate overfitting in the generation of adversarial examples and improve their transferability. In light of this phenomenon, this paper proposes an adversarial example generation method, which can be integrated with Fast Gradient Sign Method (FGSM)-related methods to build a more robust gradient-based attack and to generate adversarial examples with better transferability. Extensive experiments on the ImageNet dataset have demonstrated the effectiveness of the aforementioned method. Whether on normally or adversarially trained networks, our method has a higher success rate for black-box attacks than other attack methods based on data augmentation. It is hoped that this method can help evaluate and improve the robustness of models.

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