Neural Network Image Recognition Robustness with Different Augmentation Methods

Ziyadinov, Vadim; Терешонок, М. В.

doi:10.1109/synchroinfo55067.2022.9840987

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…This leads to a recognition accuracy decrease, as CNN is initially trained to recognize sharp images. Thus, filtering images with a Gaussian filter allows us to reduce the problem of overcoming high-frequency adversarial attacks to the problem of blurred image recognition, considered in our previous work [38]. The essence of the proposed technique is shown in Figure 2.…”

Section: Introductionmentioning

confidence: 99%

Low-Pass Image Filtering to Achieve Adversarial Robustness

Ziyadinov,

Tereshonok

2023

Preprint

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In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness [1]. Currently, a popular research area related to artificial neural networks is adversarial attacks. The effect of adversarial attacks on the image is not highly perceptible to the human eye, also it drastically reduces the neural network accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. The approach proposed in this paper can improve the image recognition accuracy in the presence of high-frequency distortions, in particular, caused by adversarial attacks. The proposed technique makes it possible to measure up the logic of artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition.

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Section: Introductionmentioning

confidence: 99%

Low-Pass Image Filtering to Achieve Adversarial Robustness

Ziyadinov,

Tereshonok

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This leads to a recognition accuracy decrease, as a CNN is initially trained to recognize sharp images. Thus, filtering images with a Gaussian filter allows us to reduce the problem of overcoming high-frequency adversarial attacks to the problem of blurred image recognition, considered in our previous work [41]. We perform a large set of tests for FGSM intensities and Gaussian filter sizes.…”

Section: Introductionmentioning

confidence: 99%

Low-Pass Image Filtering to Achieve Adversarial Robustness

Ziyadinov,

Tereshonok

2023

Sensors

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In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness. Currently, a popular research area related to artificial neural networks is adversarial attacks. The adversarial attacks on the image are not highly perceptible to the human eye, and they also drastically reduce the neural network’s accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. We propose a technique to reduce the influence of high-frequency noise on the CNNs. We show that low-pass image filtering can improve the image recognition accuracy in the presence of high-frequency distortions in particular, caused by adversarial attacks. This technique is resource efficient and easy to implement. The proposed technique makes it possible to measure up the logic of an artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition.

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Optimizing Malware Detection for IoT and Edge Environments With Quantization Awareness

Alandjani

2024

IEEE Access

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Neural Network Image Recognition Robustness with Different Augmentation Methods

Cited by 3 publications

References 14 publications

Low-Pass Image Filtering to Achieve Adversarial Robustness

Low-Pass Image Filtering to Achieve Adversarial Robustness

Low-Pass Image Filtering to Achieve Adversarial Robustness

Optimizing Malware Detection for IoT and Edge Environments With Quantization Awareness

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