{"id":475822,"date":"2023-08-09T07:23:51","date_gmt":"2023-08-09T07:23:51","guid":{"rendered":""},"modified":"2023-09-05T11:11:17","modified_gmt":"2023-09-05T11:11:17","slug":"adversarial-machine-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/ar\/wiki\/adversarial-machine-learning\/","title":{"rendered":"\u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0627\u0644\u0639\u062f\u0627\u0626\u064a"},"content":{"rendered":"<p>\u064a\u0639\u062f \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0627\u0644\u062a\u0646\u0627\u0641\u0633\u064a \u0645\u062c\u0627\u0644\u064b\u0627 \u0645\u062a\u0637\u0648\u0631\u064b\u0627 \u064a\u0642\u0639 \u0639\u0646\u062f \u062a\u0642\u0627\u0637\u0639 \u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a \u0648\u0627\u0644\u0623\u0645\u0646 \u0627\u0644\u0633\u064a\u0628\u0631\u0627\u0646\u064a. \u0648\u064a\u0631\u0643\u0632 \u0639\u0644\u0649 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\u0627\u0644\u0647\u062c\u0645\u0627\u062a \u0639\u0644\u0649 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a.<\/td>\n<\/tr>\n<tr>\n<td>\u0627\u0644\u0623\u0645\u0646 \u0627\u0644\u0625\u0644\u0643\u062a\u0631\u0648\u0646\u064a<\/td>\n<td>\u064a\u0634\u0645\u0644 \u0627\u0644\u062a\u0642\u0646\u064a\u0627\u062a \u0648\u0627\u0644\u0645\u0645\u0627\u0631\u0633\u0627\u062a \u0644\u062d\u0645\u0627\u064a\u0629 \u0623\u0646\u0638\u0645\u0629 \u0627\u0644\u0643\u0645\u0628\u064a\u0648\u062a\u0631 \u0645\u0646 \u0627\u0644\u0647\u062c\u0645\u0627\u062a \u0648\u0627\u0644\u062a\u0647\u062f\u064a\u062f\u0627\u062a.<\/td>\n<\/tr>\n<tr>\n<td>\u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0627\u0644\u064a<\/td>\n<td>\u064a\u062a\u0636\u0645\u0646 \u0627\u0644\u062e\u0648\u0627\u0631\u0632\u0645\u064a\u0627\u062a \u0648\u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a\u0629 \u0627\u0644\u062a\u064a 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\u0627\u0644\u0642\u0648\u0629 \u0627\u0644\u0645\u0633\u062a\u0646\u062f\u0629 \u0625\u0644\u0649 \u0627\u0644\u0633\u062d\u0627\u0628\u0629 \u0644\u0644\u0634\u0631\u0643\u0627\u062a \u0644\u062a\u0623\u0645\u064a\u0646 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u0647\u0627.<\/p>\n<\/li>\n<\/ol>\n<h2>\u0643\u064a\u0641 \u064a\u0645\u0643\u0646 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u0623\u0648 \u0631\u0628\u0637\u0647\u0627 \u0628\u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0627\u0644\u062a\u0646\u0627\u0641\u0633\u064a<\/h2>\n<p>\u062a\u0644\u0639\u0628 \u0627\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u062f\u0648\u0631\u064b\u0627 \u062d\u0627\u0633\u0645\u064b\u0627 \u0641\u064a \u062a\u0639\u0632\u064a\u0632 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\u0627\u0644\u062a\u0646\u0627\u0641\u0633\u064a \u0628\u0627\u0644\u0637\u0631\u0642 \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/p>\n<ol>\n<li>\n<p><strong>\u062d\u0645\u0627\u064a\u0629 \u0627\u0644\u0628\u0646\u064a\u0629 \u0627\u0644\u062a\u062d\u062a\u064a\u0629 \u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u0629<\/strong>: \u064a\u0645\u0643\u0646 \u0644\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u062d\u0645\u0627\u064a\u0629 \u0627\u0644\u0628\u0646\u064a\u0629 \u0627\u0644\u062a\u062d\u062a\u064a\u0629 \u0644\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0645\u0646 \u0627\u0644\u0647\u062c\u0645\u0627\u062a \u0627\u0644\u0645\u0628\u0627\u0634\u0631\u0629 \u0648\u0645\u062d\u0627\u0648\u0644\u0627\u062a \u0627\u0644\u0648\u0635\u0648\u0644 \u063a\u064a\u0631 \u0627\u0644\u0645\u0635\u0631\u062d \u0628\u0647\u0627.<\/p>\n<\/li>\n<li>\n<p><strong>\u0627\u0644\u062f\u0641\u0627\u0639 \u0636\u062f \u0627\u0644\u0647\u062c\u0645\u0627\u062a \u0627\u0644\u0639\u062f\u0627\u0626\u064a\u0629<\/strong>: \u064a\u0645\u0643\u0646 \u0644\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u062a\u062d\u0644\u064a\u0644 \u062d\u0631\u0643\u0629 \u0627\u0644\u0645\u0631\u0648\u0631 \u0627\u0644\u0648\u0627\u0631\u062f\u0629 \u0628\u062d\u062b\u064b\u0627 \u0639\u0646 \u0623\u0646\u0634\u0637\u0629 \u0639\u062f\u0627\u0626\u064a\u0629 \u0645\u062d\u062a\u0645\u0644\u0629\u060c \u0648\u062a\u0635\u0641\u064a\u0629 \u0627\u0644\u0637\u0644\u0628\u0627\u062a \u0627\u0644\u0636\u0627\u0631\u0629 \u0642\u0628\u0644 \u0623\u0646 \u062a\u0635\u0644 \u0625\u0644\u0649 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a.<\/p>\n<\/li>\n<li>\n<p><strong>\u062d\u0645\u0627\u064a\u0629 \u0627\u0644\u062e\u0635\u0648\u0635\u064a\u0629<\/strong>: \u064a\u0645\u0643\u0646 \u0623\u0646 \u062a\u0633\u0627\u0639\u062f \u0627\u0644\u062e\u0648\u0627\u062f\u0645 \u0627\u0644\u0648\u0643\u064a\u0644\u0629 \u0641\u064a \u0625\u062e\u0641\u0627\u0621 \u0647\u0648\u064a\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0648\u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0627\u0644\u0645\u0633\u062a\u062e\u062f\u0645\u060c \u0645\u0645\u0627 \u064a\u0642\u0644\u0644 \u0645\u0646 \u0645\u062e\u0627\u0637\u0631 \u0647\u062c\u0645\u0627\u062a \u062a\u0633\u0645\u064a\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0645\u062d\u062a\u0645\u0644\u0629.<\/p>\n<\/li>\n<\/ol>\n<h2>\u0631\u0648\u0627\u0628\u0637 \u0630\u0627\u062a \u0639\u0644\u0627\u0642\u0629<\/h2>\n<p>\u0644\u0645\u0632\u064a\u062f \u0645\u0646 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a \u062d\u0648\u0644 \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a \u0627\u0644\u062a\u0646\u0627\u0641\u0633\u064a\u060c \u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u0643\u0634\u0627\u0641 \u0627\u0644\u0645\u0648\u0627\u0631\u062f \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/p>\n<ol>\n<li><a href=\"https:\/\/openai.com\/blog\/adversarial-example-research\/\" target=\"_new\" rel=\"noopener nofollow\">\u0645\u062f\u0648\u0646\u0629 OpenAI \u2013 \u0623\u0645\u062b\u0644\u0629 \u0627\u0644\u062e\u0635\u0648\u0645\u0629<\/a><\/li>\n<li><a href=\"https:\/\/ai.googleblog.com\/2019\/03\/explaining-and-harnessing-adversarial.html\" target=\"_new\" rel=\"noopener nofollow\">\u0645\u062f\u0648\u0646\u0629 Google AI \u2013 \u0634\u0631\u062d \u0627\u0644\u0623\u0645\u062b\u0644\u0629 \u0627\u0644\u062a\u0646\u0627\u0641\u0633\u064a\u0629 \u0648\u0627\u0644\u0627\u0633\u062a\u0641\u0627\u062f\u0629 \u0645\u0646\u0647\u0627<\/a><\/li>\n<li><a href=\"https:\/\/www.technologyreview.com\/2021\/05\/25\/1025127\/the-ai-detectives\/\" target=\"_new\" rel=\"noopener nofollow\">\u0625\u0645 \u0622\u064a \u062a\u064a \u062a\u0643\u0646\u0648\u0644\u0648\u062c\u064a \u0631\u064a\u0641\u064a\u0648 \u2013 \u0645\u062d\u0642\u0642\u0648 \u0627\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a<\/a><\/li>\n<\/ol>","protected":false},"featured_media":0,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475822","wiki","type-wiki","status-publish","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Adversarial Machine Learning: Enhancing Proxy Server Security<\/mark>","faq_items":[{"question":"What is Adversarial Machine Learning?","answer":"<p>Adversarial Machine Learning is a field that focuses on understanding and countering adversarial attacks on machine learning models. It aims to build robust and resilient AI systems that can defend against attempts to deceive or compromise their performance.<\/p>"},{"question":"How did Adversarial Machine Learning originate?","answer":"<p>The concept of Adversarial Machine Learning emerged in the early 2000s when researchers noticed vulnerabilities in machine learning algorithms. The first mention of adversarial attacks can be traced back to the work of Szegedy et al. in 2013, where they demonstrated the existence of adversarial examples.<\/p>"},{"question":"How does Adversarial Machine Learning work?","answer":"<p>Adversarial Machine Learning involves two key components: the adversary and the defender. The adversary crafts adversarial examples, while the defender designs robust models to withstand these attacks. Adversarial examples are perturbed inputs that aim to mislead the target machine learning model.<\/p>"},{"question":"What are the key features of Adversarial Machine Learning?","answer":"<p>The key features of Adversarial Machine Learning include the existence of adversarial examples, their transferability between models, and the trade-off between robustness and accuracy. Additionally, adversaries use sophisticated attacks, such as white-box, black-box, transfer, and physical-world attacks.<\/p>"},{"question":"What types of Adversarial Machine Learning attacks exist?","answer":"<p>Adversarial attacks come in various forms:<\/p><ul><li>White-box Attacks: The attacker has complete access to the model's architecture and parameters.<\/li><li>Black-box Attacks: The attacker has limited access to the target model and may use substitute models.<\/li><li>Transfer Attacks: Adversarial examples generated for one model are used to attack another model.<\/li><li>Physical-world Attacks: Adversarial examples designed to work in real-world scenarios, such as fooling autonomous vehicles.<\/li><\/ul>"},{"question":"How can Adversarial Machine Learning be used?","answer":"<p>Adversarial Machine Learning finds applications in computer vision, natural language processing, and cybersecurity. It helps enhance the security of AI models and protects against potential threats posed by adversarial attacks.<\/p>"},{"question":"What are the challenges in using Adversarial Machine Learning?","answer":"<p>Some challenges include ensuring robustness against novel attacks, dealing with computational overhead, and maintaining data quality when handling adversarial examples.<\/p>"},{"question":"How does Adversarial Machine Learning compare to other terms?","answer":"<p>Adversarial Machine Learning is related to cybersecurity, machine learning, and artificial intelligence (AI), but it specifically focuses on defending machine learning models against adversarial attacks.<\/p>"},{"question":"What does the future hold for Adversarial Machine Learning?","answer":"<p>The future of Adversarial Machine Learning includes advancements in attack and defense techniques, leveraging GANs, developing interpretable models, and providing robustness as a service.<\/p>"},{"question":"How are proxy servers associated with Adversarial Machine Learning?","answer":"<p>Proxy servers play a vital role in enhancing security by protecting ML infrastructure, defending against adversarial attacks, and safeguarding user privacy and data. They act as intermediaries, filtering out potential malicious traffic before it reaches the machine learning model.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki\/475822","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/wiki\/475822\/revisions"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/ar\/wp-json\/wp\/v2\/media?parent=475822"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}