Fighting fire with fire: A spatial–frequency ensemble relation network with generative adversarial learning for adversarial image classification

2021 ◽  
Author(s):  
Wenbo Zheng ◽  
Lan Yan ◽  
Chao Gou ◽  
Fei‐Yue Wang
Keyword(s):  
Spatial Frequency ◽  
Image Classification ◽  
Adversarial Learning

scholarly journals Applying LCS To Affective Image Classification In Spatial-Frequency Domain

2014 ◽  
Vol 4 (2) ◽  
pp. 99-123 ◽  
Author(s):  
Po-Ming Lee ◽  
Tzu-Chien Hsiao
Keyword(s):  
Pattern Recognition ◽  
Spatial Frequency ◽  
Image Classification ◽  
Frequency Domain ◽  
Model Building ◽  
Experimental Paradigm ◽  
Wide Range ◽  
Classification Tasks ◽  
Affective Image Classification ◽  
Spatial Frequency Domain

Abstract Recent studies have utilizes color, texture, and composition information of images to achieve affective image classification. However, the features related to spatial-frequency domain that were proven to be useful for traditional pattern recognition have not been tested in this field yet. Furthermore, the experiments conducted by previous studies are not internationally-comparable due to the experimental paradigm adopted. In addition, contributed by recent advances in methodology, that are, Hilbert-Huang Transform (HHT) (i.e. Empirical Mode Decomposition (EMD) and Hilbert Transform (HT)), the resolution of frequency analysis has been improved. Hence, the goal of this research is to achieve the affective image-classification task by adopting a standard experimental paradigm introduces by psychologists in order to produce international-comparable and reproducible results; and also to explore the affective hidden patterns of images in the spatial-frequency domain. To accomplish these goals, multiple human-subject experiments were conducted in laboratory. Extended Classifier Systems (XCSs) was used for model building because the XCS has been applied to a wide range of classification tasks and proved to be competitive in pattern recognition. To exploit the information in the spatial-frequency domain, the traditional EMD has been extended to a two-dimensional version. To summarize, the model built by using the XCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the XCS was compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used for classification tasks. Contributed by proper selection of features for model building, user-independent findings were obtained. For example, it is found that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation. The effect of hue, saturation, and brightness; is also presented.

Fourier spatial frequency analysis for image classification: training the training set

2016 ◽  
Author(s):  
Timothy H. Johnson ◽  
Yigah Lhamo ◽  
Lingyan Shi ◽  
Robert R. Alfano ◽  
Stewart Russell
Keyword(s):  
Spatial Frequency ◽  
Image Classification ◽  
Frequency Analysis ◽  
Training Set ◽  
Spatial Frequency Analysis

scholarly journals Adversarial Learning With Knowledge of Image Classification for Improving GANs

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 56591-56605
Author(s):  
Jae-Yong Baek ◽  
Yong-Sang Yoo ◽  
Seung-Hwan Bae
Keyword(s):  
Image Classification ◽  
Adversarial Learning

A Comparative Study on Adversarial Noise Generation for Single Image Classification

2020 ◽  
Vol 16 (1) ◽  
pp. 75-87
Author(s):  
Rishabh Saxena ◽  
Amit Sanjay Adate ◽  
Don Sasikumar
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Saliency Map ◽  
Use Case ◽  
Noise Generation ◽  
Single Image ◽  
Adversarial Learning ◽  
Map Algorithm ◽  
Fast Gradient ◽  
Sign Method

With the rise of neural network-based classifiers, it is evident that these algorithms are here to stay. Even though various algorithms have been developed, these classifiers still remain vulnerable to misclassification attacks. This article outlines a new noise layer attack based on adversarial learning and compares the proposed method to other such attacking methodologies like Fast Gradient Sign Method, Jacobian-Based Saliency Map Algorithm and DeepFool. This work deals with comparing these algorithms for the use case of single image classification and provides a detailed analysis of how each algorithm compares to each other.

scholarly journals Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

2020 ◽  
Vol 58 (6) ◽  
pp. 3791-3808 ◽  
Author(s):  
Danfeng Hong ◽  
Xin Wu ◽  
Pedram Ghamisi ◽  
Jocelyn Chanussot ◽  
Naoto Yokoya ◽  
...  
Keyword(s):  
Spatial Frequency ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Feature Extractor

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

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