{"id":252732,"date":"2024-10-18T05:30:00","date_gmt":"2024-10-18T05:30:00","guid":{"rendered":"https:\/\/datascientest.com\/?p=252732"},"modified":"2024-10-16T09:57:35","modified_gmt":"2024-10-16T09:57:35","slug":"adversarial-robustness-tout-savoir","status":"publish","type":"post","link":"https:\/\/datascientest.com\/adversarial-robustness-tout-savoir","title":{"rendered":"Adversarial robustness : Qu&rsquo;est-ce que c&rsquo;est ? \u00c0 quoi \u00e7a sert ?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"252732\" class=\"elementor elementor-252732\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-23f5199 e-flex e-con-boxed e-con\" data-id=\"23f5199\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-54afc30 elementor-widget elementor-widget-text-editor\" data-id=\"54afc30\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><strong>L&rsquo;adversarial robustness se concentre sur la s\u00e9curit\u00e9 des mod\u00e8les de machine learning en les rendant r\u00e9sistants aux attaques malveillantes. Il vise \u00e0 d\u00e9velopper des m\u00e9canismes de d\u00e9fense et des techniques de renforcement de la s\u00e9curit\u00e9 pour garantir que les mod\u00e8les peuvent maintenir leur performance en pr\u00e9sence d&rsquo;adversial attacks, assurant ainsi la fiabilit\u00e9 et la s\u00e9curit\u00e9 des syst\u00e8mes d&rsquo;intelligence artificielle.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9a9cd19 elementor-widget elementor-widget-text-editor\" data-id=\"9a9cd19\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Dans le domaine des techniques populaires d&rsquo;<\/span><b>apprentissage automatique<\/b><span style=\"font-weight: 400;\">, les avanc\u00e9es de haut niveau ont conduit \u00e0 des mod\u00e8les de pointe capables d&rsquo;accomplir des prouesses impressionnantes. L&rsquo;<\/span><b>apprentissage profond<\/b><span style=\"font-weight: 400;\">, un puissant cadre d&rsquo;apprentissage automatique, a r\u00e9volutionn\u00e9 divers domaines, de la vision par ordinateur au traitement du langage naturel. Cependant, malgr\u00e9 ces r\u00e9alisations remarquables, les mod\u00e8les d&rsquo;apprentissage profond, en particulier ceux bas\u00e9s sur l&rsquo;optimisation par gradient, sont souvent vuln\u00e9rables aux <\/span><b>adversarial attacks<\/b><span style=\"font-weight: 400;\"> (<a href=\"https:\/\/datascientest.com\/adversarial-attack-quest-ce-que-cest-et-comment-proteger-lia-contre-cette-menace\">Adversarial Attack : D\u00e9finition et protection contre cette menace (datascientest.com)<\/a>). Cette vuln\u00e9rabilit\u00e9 a conduit \u00e0 l&rsquo;\u00e9mergence d&rsquo;un domaine de recherche critique connu sous le nom d\u2019<\/span><b>adversarial robustness<\/b><span style=\"font-weight: 400;\">, qui vise \u00e0 d\u00e9velopper des techniques pour am\u00e9liorer la r\u00e9silience des mod\u00e8les d&rsquo;apprentissage automatique contre de telles attaques.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ade8aa2 e-flex e-con-boxed e-con\" data-id=\"ade8aa2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5c98bc5 elementor-widget elementor-widget-heading\" data-id=\"5c98bc5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">Qu\u2019est-ce que l\u2019adversarial training ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b1239d elementor-widget elementor-widget-text-editor\" data-id=\"0b1239d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">L&rsquo;<\/span><b>entra\u00eenement contradictoire<\/b><span style=\"font-weight: 400;\"> (ou <\/span><b>adversarial training<\/b><span style=\"font-weight: 400;\">) est au premier plan des strat\u00e9gies employ\u00e9es pour relever ce d\u00e9fi de robustesse. Le concept de l&rsquo;entra\u00eenement contradictoire consiste \u00e0 augmenter le processus d&rsquo;apprentissage \u00e0 l&rsquo;aide d&rsquo;exemples contradictoires soigneusement \u00e9labor\u00e9s. Ces exemples sont des entr\u00e9es con\u00e7ues intentionnellement pour tromper <a href=\"https:\/\/datascientest.com\/stable-diffusion-tout-savoir\">le mod\u00e8le d&rsquo;apprentissage automatique<\/a>. En exposant le mod\u00e8le \u00e0 ces exemples contradictoires pendant la formation, il devient plus robuste et apprend \u00e0 faire des pr\u00e9dictions pr\u00e9cises m\u00eame lorsqu&rsquo;il est confront\u00e9 \u00e0 des entr\u00e9es contradictoires similaires dans des sc\u00e9narios du monde r\u00e9el.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c93d49f elementor-widget elementor-widget-image\" data-id=\"c93d49f\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1000\" height=\"571\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest_1.jpg\" class=\"attachment-full size-full wp-image-252744\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest_1.jpg 1000w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest_1-300x171.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest_1-768x439.jpg 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6e94edc elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"6e94edc\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-machine-learning-engineer\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Se former au Machine Learning<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-06360a4 elementor-widget elementor-widget-text-editor\" data-id=\"06360a4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">L&rsquo;id\u00e9e cl\u00e9 de l&rsquo;entra\u00eenement contradictoire est de cr\u00e9er un ensemble de donn\u00e9es complet qui comprend \u00e0 la fois des exemples propres et des exemples contradictoires. Au cours de la formation, le mod\u00e8le est expos\u00e9 aux deux types d&rsquo;exemples, ce qui l&rsquo;oblige \u00e0 apprendre non seulement les mod\u00e8les inh\u00e9rents aux donn\u00e9es, mais aussi \u00e0 reconna\u00eetre les <\/span><b>perturbations<\/b><span style=\"font-weight: 400;\"> et \u00e0 y <\/span><b>r\u00e9sister<\/b><span style=\"font-weight: 400;\">. En r\u00e9p\u00e9tant ce processus, le mod\u00e8le devient progressivement plus r\u00e9sistant et capable de g\u00e9n\u00e9raliser les d\u00e9fenses qu&rsquo;il a apprises \u00e0 des entr\u00e9es in\u00e9dites.<\/span><\/p><p><span style=\"font-weight: 400;\">L&rsquo;un des d\u00e9fis du d\u00e9veloppement de mod\u00e8les robustes r\u00e9side dans la g\u00e9n\u00e9ration d&rsquo;exemples contradictoires efficaces. Ces exemples doivent \u00eatre soigneusement con\u00e7us pour <\/span><b>exploiter les vuln\u00e9rabilit\u00e9s du mod\u00e8le<\/b><span style=\"font-weight: 400;\"> tout en passant inaper\u00e7us aux yeux des observateurs humains. Les chercheurs ont mis au point diverses m\u00e9thodes pour g\u00e9n\u00e9rer des exemples contradictoires, telles que la m\u00e9thode du signe du gradient rapide (<\/span><b>Fast Gradient Sign Method<\/b><span style=\"font-weight: 400;\">, FGSM) et ses variantes. Ces techniques exploitent les gradients du mod\u00e8le par rapport \u00e0 l&rsquo;entr\u00e9e pour perturber it\u00e9rativement l&rsquo;entr\u00e9e de mani\u00e8re \u00e0 maximiser l&rsquo;erreur de pr\u00e9diction du mod\u00e8le.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7843852 e-flex e-con-boxed e-con\" data-id=\"7843852\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5601c58 elementor-widget elementor-widget-heading\" data-id=\"5601c58\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Quelques exemples :<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae467ca elementor-widget elementor-widget-text-editor\" data-id=\"ae467ca\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">L\u2019adversarial robustness est particuli\u00e8rement cruciale dans <\/span><b>les t\u00e2ches impliquant des images<\/b><span style=\"font-weight: 400;\">. Les <a href=\"https:\/\/datascientest.com\/algorithme-de-classification-definition-et-principaux-modeles\">mod\u00e8les de classification d&rsquo;images<\/a>, par exemple, sont largement utilis\u00e9s dans divers domaines, notamment <a href=\"http:\/\/v\u00e9hicule autonome\" data-wplink-url-error=\"true\">les v\u00e9hicules autonomes<\/a>, les diagnostics m\u00e9dicaux et les syst\u00e8mes de s\u00e9curit\u00e9. Cependant, m\u00eame les mod\u00e8les de classification d&rsquo;images les plus modernes peuvent \u00eatre tromp\u00e9s par des perturbations imperceptibles ajout\u00e9es \u00e0 l&rsquo;image d&rsquo;entr\u00e9e, ce qui entra\u00eene des <\/span><b>erreurs de classification<\/b><span style=\"font-weight: 400;\">. Les techniques d\u2019adversarial robustness visent \u00e0 r\u00e9soudre ce probl\u00e8me en formant des mod\u00e8les qui r\u00e9sistent \u00e0 de telles adversarial attacks bas\u00e9es sur l&rsquo;image.<\/span><\/p><p><span style=\"font-weight: 400;\">L\u2019adversarial robustness peut \u00e9galement s\u2019appliquer aux <\/span><b>chatbots<\/b><span style=\"font-weight: 400;\"> par exemple, afin d\u2019\u00e9viter de reproduire le sc\u00e9nario bien connu du chatbot Tay qui avait \u00e9t\u00e9 d\u00e9velopp\u00e9 par Microsoft et d\u00e9ploy\u00e9 sur Twitter pour interagir avec les utilisateurs et apprendre \u00e0 converser. Des individus malintentionn\u00e9s avait exploit\u00e9 le syst\u00e8me en nourrissant Tay d&rsquo;insultes et de propos offensants, ce qui a entra\u00een\u00e9 des comportements inappropri\u00e9s de la part du chatbot. Seulement seize heures apr\u00e8s son lancement, Microsoft a d\u00fb d\u00e9sactiver le chatbot, qui avait d\u00e9velopp\u00e9 des tendances racistes et homophobes.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9cd782d elementor-widget elementor-widget-image\" data-id=\"9cd782d\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1000\" height=\"571\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest_2.jpg\" class=\"attachment-full size-full wp-image-252745\" alt=\"\" srcset=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest_2.jpg 1000w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest_2-300x171.jpg 300w, https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest_2-768x439.jpg 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8693c6a elementor-widget elementor-widget-text-editor\" data-id=\"8693c6a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Mais de nos jours, la recherche a conduit au d\u00e9veloppement de <\/span><b>m\u00e9canismes de d\u00e9fense avanc\u00e9s<\/b><span style=\"font-weight: 400;\">, y compris l&rsquo;entra\u00eenement avec diverses perturbations. Ces techniques am\u00e9liorent la capacit\u00e9 du mod\u00e8le \u00e0 r\u00e9sister aux adversarial attacks en incorporant des couches de d\u00e9fense suppl\u00e9mentaires et en exploitant les propri\u00e9t\u00e9s statistiques des donn\u00e9es. L\u2018adversarial robustness est un domaine qui \u00e9volue rapidement car les chercheurs s&rsquo;efforcent en permanence d&rsquo;am\u00e9liorer l&rsquo;efficacit\u00e9 des d\u00e9fenses contre les adversarial attacks. Les progr\u00e8s r\u00e9cents comprennent des approches qui combinent l&rsquo;entra\u00eenement contradictoire avec d&rsquo;autres techniques de r\u00e9gularisation, des m\u00e9thodes d&rsquo;ensemble qui tirent parti de la diversit\u00e9 de plusieurs mod\u00e8les et des techniques qui int\u00e8grent des mod\u00e8les g\u00e9n\u00e9ratifs pour apprendre la distribution sous-jacente des donn\u00e9es avec plus de pr\u00e9cision.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-88e7fb4 e-flex e-con-boxed e-con\" data-id=\"88e7fb4\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f927399 elementor-widget elementor-widget-heading\" data-id=\"f927399\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2eee34e elementor-widget elementor-widget-text-editor\" data-id=\"2eee34e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Bien que <a href=\"https:\/\/datascientest.com\/de-nouveaux-algorithmes-dintelligence-artificielle-voient-la-vie-en-3d\">les mod\u00e8les d&rsquo;apprentissage profond<\/a> aient connu un succ\u00e8s remarquable dans divers domaines, leur vuln\u00e9rabilit\u00e9 aux adversarial attacks reste une pr\u00e9occupation majeure. L\u2019adversarial robustness, gr\u00e2ce \u00e0 des techniques telles que l&rsquo;entra\u00eenement contradictoire (ou adversarial training), vise \u00e0 am\u00e9liorer la r\u00e9silience des mod\u00e8les d&rsquo;apprentissage automatique contre les adversarial attacks. En exposant les mod\u00e8les \u00e0 des exemples contradictoires soigneusement \u00e9labor\u00e9s pendant la formation, les chercheurs d\u00e9veloppent des strat\u00e9gies pour <\/span><b>fortifier ces mod\u00e8les et garantir leur fiabilit\u00e9 dans les sc\u00e9narios du monde r\u00e9el<\/b><span style=\"font-weight: 400;\">. Ce domaine continue d&rsquo;\u00e9voluer et promet de cr\u00e9er des mod\u00e8les d&rsquo;apprentissage automatique plus robustes et plus s\u00fbrs, ce qui permettra l&rsquo;adoption g\u00e9n\u00e9ralis\u00e9e de l&rsquo;intelligence artificielle dans des applications critiques.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-833a431 elementor-widget elementor-widget-text-editor\" data-id=\"833a431\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Vous voulez en savoir plus sur les d\u00e9fis de l\u2019intelligence artificielle ? Envie de ma\u00eetriser les techniques de <a href=\"https:\/\/datascientest.com\/deep-learning-avec-python-decouvrez-les-fondamentaux\">Deep Learning<\/a> \u00e9voqu\u00e9es dans cet article ? Renseignez-vous sur notre formation de Data Scientist (<a href=\"https:\/\/datascientest.com\/formation-data-scientist\">Formation Data Scientist | DataScientest x MINES Paris &#8211; PSL<\/a>).<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3b39705 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"3b39705\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-machine-learning-engineer\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Se former \u00e0 l'adversarial robustness<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e118638 e-flex e-con-boxed e-con\" data-id=\"e118638\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3dfde82 elementor-widget elementor-widget-html\" data-id=\"3dfde82\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Qu'est-ce que l'Adversarial Robustness ?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"L'Adversarial Robustness fait r\u00e9f\u00e9rence \u00e0 la capacit\u00e9 d'un mod\u00e8le d'apprentissage automatique \u00e0 r\u00e9sister \u00e0 des exemples adversariaux, qui sont des entr\u00e9es intentionnellement modifi\u00e9es pour tromper le mod\u00e8le et produire des pr\u00e9dictions incorrectes. Cela permet d'am\u00e9liorer la s\u00e9curit\u00e9 et la fiabilit\u00e9 des mod\u00e8les, notamment dans des domaines critiques comme la vision par ordinateur et le traitement du langage naturel.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Quels sont les risques associ\u00e9s aux exemples adversariaux ?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Les exemples adversariaux peuvent entra\u00eener des erreurs de pr\u00e9diction co\u00fbteuses dans des applications sensibles, telles que les voitures autonomes, les syst\u00e8mes de s\u00e9curit\u00e9 biom\u00e9trique ou les diagnostics m\u00e9dicaux. En exploitant des faiblesses du mod\u00e8le, les attaquants peuvent provoquer des comportements ind\u00e9sirables ou compromettre la fiabilit\u00e9 des syst\u00e8mes intelligents.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Comment am\u00e9liorer l'adversarial robustness d'un mod\u00e8le d'apprentissage automatique ?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Pour am\u00e9liorer l'adversarial robustness, plusieurs techniques peuvent \u00eatre utilis\u00e9es, telles que l'entra\u00eenement avec des exemples adversariaux, l'application de r\u00e9gularisation, l'utilisation de r\u00e9seaux neuronaux d\u00e9fensifs ou la d\u00e9tection des attaques adversariales. Ces m\u00e9thodes visent \u00e0 rendre les mod\u00e8les plus r\u00e9sistants face \u00e0 des perturbations intentionnelles.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"Quels secteurs sont concern\u00e9s par l'adversarial robustness ?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"L'adversarial robustness est cruciale dans plusieurs secteurs critiques tels que la cybers\u00e9curit\u00e9, la finance, l'industrie automobile (pour les voitures autonomes), la sant\u00e9 (diagnostics automatis\u00e9s), et les technologies de d\u00e9fense. Toute application utilisant l'IA dans des contextes \u00e0 fort enjeu doit consid\u00e9rer ces vuln\u00e9rabilit\u00e9s potentielles.\"\r\n      }\r\n    }\r\n  ]\r\n}\r\n<\/script>\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>L&rsquo;adversarial robustness se concentre sur la s\u00e9curit\u00e9 des mod\u00e8les de machine learning en les rendant r\u00e9sistants aux attaques malveillantes. Il vise \u00e0 d\u00e9velopper des m\u00e9canismes de d\u00e9fense et des techniques de renforcement de la s\u00e9curit\u00e9 pour garantir que les mod\u00e8les peuvent maintenir leur performance en pr\u00e9sence d&rsquo;adversial attacks, assurant ainsi la fiabilit\u00e9 et la s\u00e9curit\u00e9 [&hellip;]<\/p>\n","protected":false},"author":78,"featured_media":252734,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497,2422],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Adversarial robustness : Qu&#039;est-ce que c&#039;est ? \u00c0 quoi \u00e7a sert ?<\/title>\n<meta name=\"description\" content=\"D\u00e9couvrez comment l&#039;Adversarial Robustness renforce la s\u00e9curit\u00e9 des mod\u00e8les d&#039;IA en les rendant r\u00e9sistants aux attaques adversariales, garantissant des pr\u00e9dictions fiables et prot\u00e9g\u00e9es.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/adversarial-robustness-tout-savoir\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Adversarial robustness : Qu&#039;est-ce que c&#039;est ? \u00c0 quoi \u00e7a sert ?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/adversarial-robustness-tout-savoir\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-18T05:30:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-10-16T09:57:35+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2024\/09\/Adversarial_Robustness_DataScientest.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"600\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"J\u00e9r\u00e9my Robert\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"J\u00e9r\u00e9my Robert\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/adversarial-robustness-tout-savoir\",\"url\":\"https:\/\/datascientest.com\/adversarial-robustness-tout-savoir\",\"name\":\"Adversarial robustness : Qu'est-ce que c'est ? \u00c0 quoi \u00e7a sert ?\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2024-10-18T05:30:00+00:00\",\"dateModified\":\"2024-10-16T09:57:35+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\"},\"description\":\"D\u00e9couvrez comment l'Adversarial Robustness renforce la s\u00e9curit\u00e9 des mod\u00e8les d'IA en les rendant r\u00e9sistants aux attaques adversariales, garantissant des pr\u00e9dictions fiables et prot\u00e9g\u00e9es.\",\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/adversarial-robustness-tout-savoir\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/#website\",\"url\":\"https:\/\/datascientest.com\/\",\"name\":\"Formation Data Science | DataScientest.com\",\"description\":\"Leader de la formation en Data Science\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"fr-FR\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/7af2361c48e24e79a8aa2360e6e74925\",\"name\":\"J\u00e9r\u00e9my Robert\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"fr-FR\",\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"contentUrl\":\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/11\/jeremyrobert.jpeg\",\"caption\":\"J\u00e9r\u00e9my Robert\"},\"description\":\"J\u00e9r\u00e9my Robert est actuellement Directeur p\u00e9dagogique B2B chez DataScientest.com. 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