scholarly journals The Theory of Random Transformation of Dispersed Matter

10.5772/52369 ◽  
2013 ◽  
Author(s):  
Marek Solecki
Keyword(s):  
Random Transformation

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.

scholarly journals A Double-Image Encryption Scheme Based on Amplitude-Phase Encoding and Discrete Complex Random Transformation

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 77740-77753 ◽  
Author(s):  
Yuling Luo ◽  
Shunbin Tang ◽  
Xingsheng Qin ◽  
Lvchen Cao ◽  
Frank Jiang ◽  
...  
Keyword(s):  
Image Encryption ◽  
Encryption Scheme ◽  
Phase Encoding ◽  
Double Image ◽  
Random Transformation ◽  
Discrete Complex

scholarly journals Culture without copying or selection

2021 ◽  
pp. 1-31
Author(s):  
Alberto Acerbi ◽  
Mathieu Charbonneau ◽  
Helena Miton ◽  
Thom Scott-Phillips
Keyword(s):  
Cultural Evolution ◽  
Stochastic Models ◽  
Selection Process ◽  
Population Level ◽  
Fundamental Question ◽  
High Fidelity ◽  
Formal Support ◽  
Random Transformation ◽  
Degree Of Similarity

Abstract Typical examples of cultural phenomena all exhibit a degree of similarity across time and space at the level of the population. As such, a fundamental question for any science of culture is, what ensures this stability in the first place? Here we focus on the evolutionary and stabilizing role of ‘convergent transformation’, in which one item causes the production of another item whose form tends to deviate from the original in a directed, non-random way. We present a series of stochastic models of cultural evolution investigating its effects. Results show that cultural stability can emerge and be maintained by virtue of convergent transformation alone, in the absence of any form of copying or selection process. We show how high-fidelity copying and convergent transformation need not be opposing forces, and can jointly contribute to cultural stability. We finally analyse how non-random transformation and high-fidelity copying can have different evolutionary signatures at population level, and hence how their distinct effects can be distinguished in empirical records. Collectively, these results supplement existing approaches to cultural evolution based on the Darwinian analogy, while also providing formal support for other frameworks — such as Cultural Attraction Theory — that entail its further loosening.

scholarly journals The Optimization of Solutions of the Dynamic Systems with Random Structure

2011 ◽  
Vol 2011 ◽  
pp. 1-18 ◽  
Author(s):  
Miroslava Růžičková ◽  
Irada Dzhalladova
Keyword(s):  
Optimal Control ◽  
Control System ◽  
Random Process ◽  
Control Systems ◽  
Dynamic Systems ◽  
Lyapunov Functions ◽  
Necessary Conditions ◽  
Random Structure ◽  
Random Transformation ◽  
Linear Differential

The paper deals with the class of jump control systems with semi-Markov coefficients. The control system is described as the system of linear differential equations. Every jump of the random process implies the random transformation of solutions of the considered system. Relations determining the optimal control to minimize the functional are derived using Lyapunov functions. Necessary conditions of optimization which enables the synthesis of the optimal control are established as well.

Sign in / Sign up

Export Citation Format

Share Document