title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð´Ð»Ñ Ð±Ð¾ÑÑÐ±Ñ Ñ ÑÑÐ¼Ð¾Ð¼ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Application of neural networks to combat noise in images
Creators: name: Ð¢ÐµÐ»ÑÐ½Ð¾Ð²Ð°, Ð¢Ð°ÑÑÑÐ½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¿Ð°ÑÐ°Ð·Ð¸ÑÐ½ÑÐ¹ ÑÑÐ¼
Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ðµ
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
adversarial noise
image
convolutional neural networks

description: Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ð° ÑÐ¼ÐµÐ½ÑÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð° Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½ÑÑ Ð¾ÑÐ²ÐµÑÐ¾Ð² ÑÐ¸ÑÑÐµÐ¼Ñ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¸ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð¿Ð°ÑÐ°Ð·Ð¸ÑÐ½ÑÐ¼ ÑÑÐ¼Ð¾Ð¼. Ð¦ÐµÐ»Ñ â ÑÐµÑÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð¿ÑÑÐµÐ¼ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð¼ÐµÑÐ¾Ð´Ð° Ð±Ð¾ÑÑÐ±Ñ Ñ ÑÑÐ¼Ð¾Ð¼ Ð½Ð° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸ÑÑ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ ÐºÐ°Ðº Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¼Ð¾ÑÐ½ÑÑ Ð¸ ÑÐ½Ð¸Ð²ÐµÑÑÐ°Ð»ÑÐ½ÑÑ Ð¸Ð½ÑÑÑÑÐ¼ÐµÐ½ÑÐ¾Ð² ÑÐ°Ð±Ð¾ÑÑ Ñ ÑÐ°ÐºÐ¸Ð¼ ÑÐ¸Ð¿Ð¾Ð¼ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ ÑÐ¿Ð¾ÑÐ¾Ð±Ð¾Ð² ÑÑÐ¼Ð¾Ð¿Ð¾Ð´Ð°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð», ÑÑÐ¾ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÑÐ¼ ÑÐ²Ð»ÑÐµÑÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð°Ð²ÑÐ¾ÑÐ½ÐºÐ¾Ð´ÐµÑÐ°. ÐÐ³Ð¾ Ð±Ð°Ð·Ð¾Ð²Ð°Ñ Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° Ð±ÑÐ»Ð° Ð²Ð·ÑÑÐ° Ð·Ð° Ð¾ÑÐ½Ð¾Ð²Ñ Ð² ÑÐ°Ð·ÑÐ°Ð±Ð°ÑÑÐ²Ð°ÐµÐ¼Ð¾Ð¼ Ð¼ÐµÑÐ¾Ð´Ðµ ÑÑÐ¼Ð¾Ð¿Ð¾Ð´Ð°Ð²Ð»ÐµÐ½Ð¸Ñ. ÐÑÐµÐ½ÐºÐ° Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑ Ð¿ÑÑÐµÐ¼ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ð´Ð¾Ð»Ð¸ Ð¿ÑÐ°Ð²Ð¸Ð»ÑÐ½ÑÑ Ð¾ÑÐ²ÐµÑÐ¾Ð² Ð¿ÑÐ¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÑÐ¼Ð¾Ð¿Ð¾Ð´Ð°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¸ Ð±ÐµÐ·. ÐÐ¾ÑÑÐ¸Ð³Ð½ÑÑÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ â Ð¸ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð¾ÑÐ¸Ð±Ð¾Ðº ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð´Ð»Ñ 63% Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ñ ÑÑÐ¼Ð¾Ð¼. ÐÐµÑÐ¾Ð´ Ð±ÑÐ» Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½ Ð² Ð¼Ð¾Ð´ÑÐ»Ðµ Ð¿Ð¾Ð´Ð°Ð²Ð»ÐµÐ½Ð¸Ñ ÑÑÐ¼Ð° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐµÐ³Ð¾ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸ÑÑ Ð¿ÑÐµÐ´Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÑ Ð´Ð°Ð½Ð½ÑÑ Ð¸ Ð¾ÑÐµÐ½ÐºÑ ÑÑÑÐ¾Ð¹ÑÐ¸Ð²Ð¾ÑÑÐ¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ðº Ð¿Ð°ÑÐ°Ð·Ð¸ÑÐ½Ð¾Ð¼Ñ ÑÑÐ¼Ñ Ð¸ Ð°Ð½Ð°Ð»Ð¾Ð³Ð¸ÑÐ½ÑÐ¼ Ð¸ÑÐºÐ°Ð¶ÐµÐ½Ð¸ÑÐ¼.
description: The paper considers the problem of reducing the number of correct predictions when computer vision systems process adversarial images. The main purpose is to find a way to solve the problem by developing a method for dealing with adversarial noise in images by neural networks as powerful and universal tools for working with such data. The analysis of existing adversarial noise reduction methods showed that the most effective is the use of an autoencoder. Its basic architecture was taken as the basis in the developed noise reduction method. The evaluation was carried out by comparing the proportion of correct predictions for image classification with and without noise reduction. The results achieved are the correction of classification errors for 63% of images with noise. The method was used in the noise reduction modÂ¬ule of the software, which allows one to evaluate the robustness of a neural network to spurious noise and similar distortions.