{"id":4816,"date":"2022-12-01T19:24:31","date_gmt":"2022-12-02T00:24:31","guid":{"rendered":"http:\/\/skimai.com\/?p=4816"},"modified":"2024-04-29T17:27:40","modified_gmt":"2024-04-29T22:27:40","slug":"blog-quest-ce-quune-attaque-informatique-contradictoire","status":"publish","type":"post","link":"https:\/\/skimai.com\/fr\/blog-what-is-an-adversarial-ai-attack\/","title":{"rendered":"Qu'est-ce qu'une attaque d'IA accusatoire ?"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_1 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table des mati\u00e8res<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table des mati\u00e8res\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1 ez-toc-heading-level-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-an-adversarial-ai-attack\/#What_is_an_Adversarial_AI_Attack\" >Qu'est-ce qu'une attaque d'IA accusatoire ?<\/a><ul class='ez-toc-list-level-2' ><li class='ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-an-adversarial-ai-attack\/#Risks_posed_by_an_Adversarial_AI_attack\" >Risques pos\u00e9s par une attaque de l'IA adverse<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-an-adversarial-ai-attack\/#Examples_of_Adversarial_AI_Attacks\" >Exemples d'attaques d'IA adverses<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-an-adversarial-ai-attack\/#How_to_defend_against_Adversarial_AI_attacks\" >Comment se d\u00e9fendre contre les attaques de l'intelligence artificielle<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/skimai.com\/fr\/blog-what-is-an-adversarial-ai-attack\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h1><span class=\"ez-toc-section\" id=\"What_is_an_Adversarial_AI_Attack\"><\/span>Qu'est-ce qu'une attaque d'IA accusatoire ?<span class=\"ez-toc-section-end\"><\/span><\/h1>\n<p>Les technologies d'apprentissage profond telles que l'intelligence artificielle (IA) et l'apprentissage machine (ML) pr\u00e9sentent une s\u00e9rie d'avantages pour les entreprises. Dans le m\u00eame temps, une attaque de l'IA constitue un d\u00e9fi de s\u00e9curit\u00e9 croissant pour les syst\u00e8mes modernes.<br \/>\nQu'est-ce qu'une attaque d'IA adversive ? Il s'agit d'une forme de cyberattaque qui \"trompe\" les mod\u00e8les d'IA \u00e0 l'aide de donn\u00e9es trompeuses. \u00c9galement connue sous le nom d'Adversarial ML, cette forme d'attaque a \u00e9t\u00e9 largement signal\u00e9e dans les domaines de la classification d'images et de la d\u00e9tection de spams.<br \/>\nEnsuite, examinons les diff\u00e9rents risques pos\u00e9s par une attaque d'IA adverses.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Risks_posed_by_an_Adversarial_AI_attack\"><\/span>Risques pos\u00e9s par une attaque de l'IA adverse<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Selon le <a href=\"https:\/\/www.csoonline.com\/article\/3664748\/adversarial-machine-learning-explained-how-attackers-disrupt-ai-and-ml-systems.html\">Alexey Rubtsov<\/a> de l'Institut mondial du risque, \"l'apprentissage automatique antagoniste exploite les vuln\u00e9rabilit\u00e9s et les sp\u00e9cificit\u00e9s des mod\u00e8les d'apprentissage automatique\". Par exemple, l'apprentissage automatique antagoniste peut amener un algorithme de n\u00e9gociation bas\u00e9 sur l'apprentissage automatique \u00e0 prendre des d\u00e9cisions erron\u00e9es en mati\u00e8re de n\u00e9gociation.<\/p>\n<p>En quoi une attaque d'IA adversariale repr\u00e9sente-t-elle un risque pour les syst\u00e8mes d'IA et de ML ? Voici quelques constatations courantes :<br \/>\n<strong>Ing\u00e9nierie des m\u00e9dias sociaux :<\/strong> Dans ce cas, l'attaque d'IA \"scrape\" les profils d'utilisateurs sur les plateformes de m\u00e9dias sociaux en utilisant l'automatisation. Elle g\u00e9n\u00e8re ensuite automatiquement du contenu pour \"app\u00e2ter\" le profil vis\u00e9.<\/p>\n<p><strong>Les Deepfakes :<\/strong> G\u00e9n\u00e9ralement utilis\u00e9s dans les fraudes bancaires, les \"deepfakes\" sont des acteurs de la menace en ligne qui peuvent atteindre toute une s\u00e9rie d'objectifs n\u00e9fastes :<br \/>\nChantage et extorsion<br \/>\nAtteinte \u00e0 la cr\u00e9dibilit\u00e9 et \u00e0 l'image d'une personne<br \/>\nFraude documentaire<br \/>\nM\u00e9dias sociaux pour le harc\u00e8lement individuel ou collectif<\/p>\n<p><strong>Dissimulation de logiciels malveillants :<\/strong> Dans ce cas, les acteurs de la menace s'appuient sur la ML pour dissimuler les logiciels malveillants dans un trafic \"malveillant\" qui semble normal aux yeux des utilisateurs normaux.<\/p>\n<p><strong>Mots de passe :<\/strong> Les attaques d'IA adverses peuvent m\u00eame analyser un grand nombre de mots de passe et g\u00e9n\u00e9rer des variations. En d'autres termes, ces attaques rendent le processus de craquage des mots de passe plus \"scientifique\". Outre les mots de passe, l'intelligence artificielle permet \u00e9galement aux acteurs de la menace de r\u00e9soudre les CAPTCHA.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Examples_of_Adversarial_AI_Attacks\"><\/span>Exemples d'attaques d'IA adverses<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>En utilisant des exemples d'IA adverses, les pirates peuvent simplement concevoir une attaque pour amener le mod\u00e8le d'IA \u00e0 commettre une erreur. Par exemple, une version corrompue d'une entr\u00e9e valide du mod\u00e8le.<\/p>\n<p>Voici quelques exemples de m\u00e9thodes utilis\u00e9es dans les attaques d'IA adverses :<br \/>\n<strong>M\u00e9thode du signe de gradient rapide (FastGradient Sign method - FGSM) :<\/strong> Il s'agit d'une m\u00e9thode bas\u00e9e sur le gradient qui permet de g\u00e9n\u00e9rer des exemples d'IA adversaires afin de provoquer une mauvaise classification des images. Pour ce faire, elle ajoute une quantit\u00e9 minimale de perturbations au pixel de l'image.<\/p>\n<p><strong>Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) \u00e0 m\u00e9moire limit\u00e9e :<\/strong> Cette m\u00e9thode est un autre algorithme d'optimisation num\u00e9rique non lin\u00e9aire bas\u00e9 sur le gradient qui peut minimiser le nombre de perturbations de l'image. Cette m\u00e9thode est extr\u00eamement efficace pour g\u00e9n\u00e9rer des exemples d'intelligence artificielle adverses.<\/p>\n<p><strong>Attaque de Deepfool :<\/strong> Il s'agit d'une autre m\u00e9thode tr\u00e8s efficace pour produire des attaques IA adverses avec un taux \u00e9lev\u00e9 de mauvaises classifications et moins de perturbations. Cette forme d'attaque utilise une technique de g\u00e9n\u00e9ration d'\u00e9chantillons adverses non cibl\u00e9s pour r\u00e9duire la distance euclidienne entre les \u00e9chantillons originaux et les \u00e9chantillons perturb\u00e9s.<\/p>\n<p><strong>Attaque par carte de saillance bas\u00e9e sur le jacobien (JSMA) :<\/strong> Dans cette forme d'attaque de l'intelligence artificielle, les pirates utilisent la s\u00e9lection des caract\u00e9ristiques pour minimiser le nombre de caract\u00e9ristiques modifi\u00e9es, ce qui entra\u00eene une classification erron\u00e9e. Des perturbations plates sont ajout\u00e9es it\u00e9rativement aux caract\u00e9ristiques en fonction de la valeur de saillance, par ordre d\u00e9croissant.<\/p>\n<p>Il ne s'agit l\u00e0 que de quelques exemples d'IA adverses. Voyons maintenant comment se d\u00e9fendre contre les attaques de l'IA adverse.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_to_defend_against_Adversarial_AI_attacks\"><\/span>Comment se d\u00e9fendre contre les attaques de l'intelligence artificielle<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><div id=\"attachment_6027\" style=\"width: 2491px\" class=\"wp-caption aligncenter\"><img decoding=\"async\" aria-describedby=\"caption-attachment-6027\" title=\"221122 skim ai infographic 2 png 01\"  src=\"http:\/\/skimai.com\/wp-content\/uploads\/2023\/07\/221122-skim-ai-infographic-2-png-01.png\" alt=\"221122 skim ai infographic 2 png 01\" width=\"2481\" height=\"7914\" \/><p id=\"caption-attachment-6027\" class=\"wp-caption-text\">221122 skim ai infographic 2 png 01<\/p><\/div><br \/>\nPour se d\u00e9fendre contre les attaques d'IA adverses, les organisations doivent adopter une approche globale de la gestion de l'intelligence artificielle. Il s'agit notamment de renforcer toutes les op\u00e9rations li\u00e9es \u00e0 l'intelligence artificielle et de surveiller en permanence toute faille dans la d\u00e9fense contre l'intelligence artificielle.<\/p>\n<p>Voici ce que doit comprendre une strat\u00e9gie de d\u00e9fense typique contre les attaques d'IA adverses :<br \/>\nCr\u00e9ation d'une \u00e9valuation des risques pour chaque mise en \u0153uvre de la ML.<br \/>\nAttribuer la propri\u00e9t\u00e9 de tous les contenus li\u00e9s au ML et la gouvernance op\u00e9rationnelle.<br \/>\nCr\u00e9ation de nouvelles politiques de s\u00e9curit\u00e9 pour se d\u00e9fendre contre les attaques de l'intelligence artificielle.<\/p>\n<p>Parmi les approches de d\u00e9fense efficaces, MLOps est utilis\u00e9 pour concevoir, d\u00e9velopper et exploiter des mod\u00e8les de ML, ainsi que pour tester et valider les mod\u00e8les. MLOps est un ensemble de bonnes pratiques qui int\u00e8gre le meilleur de l'apprentissage automatique, du DevOps et de l'ing\u00e9nierie des donn\u00e9es. Il met en \u0153uvre un processus it\u00e9ratif et incr\u00e9mental comprenant les \u00e9l\u00e9ments suivants :<br \/>\n<strong>Conception :<\/strong><br \/>\nIng\u00e9nierie des exigences<br \/>\nPriorit\u00e9 aux cas d'utilisation de la ML<br \/>\nV\u00e9rification de la disponibilit\u00e9 des donn\u00e9es<br \/>\n<strong>D\u00e9veloppement de mod\u00e8les :<\/strong><br \/>\nIng\u00e9nierie des donn\u00e9es<br \/>\nIng\u00e9nierie des mod\u00e8les ML<br \/>\nTest et validation des mod\u00e8les<br \/>\n<strong>Op\u00e9rations :<\/strong><br \/>\nD\u00e9ploiement du mod\u00e8le ML<br \/>\nD\u00e9veloppement d'un pipeline CI\/CD<br \/>\nSurveillance op\u00e9rationnelle et d\u00e9clenchement<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Selon la Commission nationale de s\u00e9curit\u00e9 des \u00c9tats-Unis <a href=\"https:\/\/science.house.gov\/imo\/media\/doc\/Schmidt%20Testimony%20Attachment.pdf\">rapport<\/a> Dans le domaine de l'intelligence artificielle, un faible pourcentage des fonds consacr\u00e9s \u00e0 la recherche sur l'intelligence artificielle est investi dans la d\u00e9fense des syst\u00e8mes d'intelligence artificielle contre les attaques adverses. L'adoption croissante de l'IA et des technologies d'apprentissage automatique est en corr\u00e9lation avec l'augmentation des attaques adverses par l'IA. En effet, les syst\u00e8mes d'IA et d'apprentissage automatique constituent une nouvelle surface d'attaque, ce qui accro\u00eet les risques li\u00e9s \u00e0 la s\u00e9curit\u00e9.<\/p>","protected":false},"excerpt":{"rendered":"<p>What is an Adversarial AI Attack? Deep learning technologies like Artificial Intelligence (AI) and machine learning (ML) present a range of business benefits to enterprises. At the same time, an adversarial AI attack is a growing security challenge for modern systems. What is an Adversarial AI attack? A form of cyberattack that \u201cfools\u201d AI models [&hellip;]<\/p>\n","protected":false},"author":1003,"featured_media":4817,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"single-custom-post-template.php","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[125],"tags":[],"class_list":["post-4816","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise-ai-blog"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What is an Adversarial AI Attack? - Skim AI<\/title>\n<meta name=\"description\" content=\"Adversarial attacks can be very deadly to a business. Avoid them at all costs. 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