SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL

Artificial intelligence (AI) has found applications across various sectors and industries, offering numerous advantages to human beings. One prominent area where AI has made significant contributions is in machine learning models. These models have revolutionized various fields, benefiting society i...

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Những tác giả chính:	Phan, Thanh Son, Ta, Quang Hua, Pham, Duy Trung, Truong, Phi Ho
Định dạng:	Bài viết
Ngôn ngữ:	English
Được phát hành:	Trường Đại học Đà Lạt 2024
Những chủ đề:	Adversarial examples Deep learning Object detection Trained model
Truy cập trực tuyến:	https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/256905 https://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/1150
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id	oai:scholar.dlu.edu.vn:DLU123456789-256905
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spelling	oai:scholar.dlu.edu.vn:DLU123456789-2569052024-12-29T01:13:09Z SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL Phan, Thanh Son Ta, Quang Hua Pham, Duy Trung Truong, Phi Ho Adversarial examples Deep learning Object detection Trained model Artificial intelligence (AI) has found applications across various sectors and industries, offering numerous advantages to human beings. One prominent area where AI has made significant contributions is in machine learning models. These models have revolutionized various fields, benefiting society in numerous ways, from self-driving cars and intelligent chatbots to automated facial authentication systems. However, in recent years, machine learning models have been the target of various attack methods. One common and dangerous attack method is adversarial attack, where modified input images can cause misclassification or erroneous predictions by the models. To confront that challenge, we present a novel approach called adversarial retraining that uses adversarial examples to train machine learning and deep learning models. This technique aims to enhance the robustness and performance of these models by subjecting them to adversarial scenarios during the training process. In this paper, we survey detection methods and propose a method to detect adversarial examples using YOLOv7, a commonly used intensive research model. By training adversarial retraining and conducting experiments, we show that the proposed method is an effective solution for helping deep learning models detect certain cases of adversarial examples. 2024-12-29T00:39:20Z 2024-12-29T00:39:20Z 2024 Article 0866-787X https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/256905 https://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/1150 10.37569/DalatUniversity.14.3.1150(2024) en Dalat University Journal of Science, Volume 14, Issue 3; p.12-26 application/pdf Trường Đại học Đà Lạt
institution	Thư viện Trường Đại học Đà Lạt
collection	Thư viện số
language	English
topic	Adversarial examples Deep learning Object detection Trained model
spellingShingle	Adversarial examples Deep learning Object detection Trained model Phan, Thanh Son Ta, Quang Hua Pham, Duy Trung Truong, Phi Ho SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL
description	Artificial intelligence (AI) has found applications across various sectors and industries, offering numerous advantages to human beings. One prominent area where AI has made significant contributions is in machine learning models. These models have revolutionized various fields, benefiting society in numerous ways, from self-driving cars and intelligent chatbots to automated facial authentication systems. However, in recent years, machine learning models have been the target of various attack methods. One common and dangerous attack method is adversarial attack, where modified input images can cause misclassification or erroneous predictions by the models. To confront that challenge, we present a novel approach called adversarial retraining that uses adversarial examples to train machine learning and deep learning models. This technique aims to enhance the robustness and performance of these models by subjecting them to adversarial scenarios during the training process. In this paper, we survey detection methods and propose a method to detect adversarial examples using YOLOv7, a commonly used intensive research model. By training adversarial retraining and conducting experiments, we show that the proposed method is an effective solution for helping deep learning models detect certain cases of adversarial examples.
format	Article
author	Phan, Thanh Son Ta, Quang Hua Pham, Duy Trung Truong, Phi Ho
author_facet	Phan, Thanh Son Ta, Quang Hua Pham, Duy Trung Truong, Phi Ho
author_sort	Phan, Thanh Son
title	SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL
title_short	SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL
title_full	SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL
title_fullStr	SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL
title_full_unstemmed	SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL
title_sort	survey and proposed method to detect adversarial examples using an adversarial retraining model
publisher	Trường Đại học Đà Lạt
publishDate	2024
url	https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/256905 https://tckh.dlu.edu.vn/index.php/tckhdhdl/article/view/1150
_version_	1819802693798985728

SURVEY AND PROPOSED METHOD TO DETECT ADVERSARIAL EXAMPLES USING AN ADVERSARIAL RETRAINING MODEL

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