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Results show extreme sensitivity, while hybrid adversarial training substantially improves robustness by boosting defense effectiveness.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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problem does the paper study about flow-based generative models?","Question",{"text":63,"@type":64},"It studies how flow-based generative models respond to adversarial perturbations, focusing on whether attackers can reduce likelihood scores for in-distribution and out-of-distribution inputs.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How do the authors analyze robustness in the paper?",{"text":68,"@type":64},"They provide a theoretical analysis for a linear flow-based generative model and then run empirical experiments on nonlinear flow models such as GLOW and RealNVP using designed adversarial attacks.",{"name":70,"@type":61,"acceptedAnswer":71},"What defense approach improves robustness according to the paper?",{"text":72,"@type":64},"A hybrid adversarial training procedure significantly boosts robustness, reducing the impact of adversarial attacks on likelihood scores.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},297221,1789742779,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & 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 \nUniversity of Maryland, College Park  \nAbstract  \nFlow-based generative models leverage invertible generator functions to 􀀌t a distribution to the training data using maximum likelihood. Despite their use in several application domains, robustness of these models to adversarial attacks has hardly been explored. In this paper, we study adversarial robustness of 􀀍ow-based generative models both theoretically (for some simple models) and empirically (for more complex ones) .  \nFirst, we consider a linear 􀀍ow-based generative model and compute optimal samplespeci􀀌c and universal adversarial perturbations that maximally decrease the likelihood scores. Using this result, we study the robustness of the well-known adversarial training procedure, where we characterize the fundamental trade-o􀀋 between model robustness and accuracy. Next, we empirically study the robustness of two prominent deep, nonlinear, 􀀍ow-based generative models, namely GLOW and RealNVP. We design two types of adversarial attacks; one that minimizes the likelihood scores of in-distribution samples, while the other that maximizes the likelihood scores of out-of-distribution ones. We 􀀌nd that GLOW and RealNVP are extremely sensitive to both types of attacks. Finally, using a hybrid adversarial training procedure, we signi􀀌cantly boost the robustness of these generative models.  \n1 Introduction  \nThe promise of modern deep generative models is to learn data distributions with su􀀎ciently high 􀀌delity,  \nProceedings of the 23rd International Conference on Arti􀀌 -cial Intelligence and Statistics (AISTATS) 2020, Palermo, Italy. PMLR: Volume 108 . Copyright 2020 by the author(s) .  \nallowing simulation of realistic samples. Some applications include photo-realistic image generation, audio synthesis, and image to text generation (Reed et al. , 2016; Ledig et al., 2017; van den Oord et al., 2016a) . Generative Adversarial Networks (GANs)(Goodfellow et al., 2014) have become a popular choice in modern generative modeling, often obtaining the state-of-theart results in image and video synthesis (Karras et al. , 2018) . While GANs can synthesize samples from a data distribution, their inability to compute sample likelihoods limits their usage in statistical inference tasks (Balaji et al., 2019) .  \nLikelihood-based models, on the other hand, explicitly 􀀌t a generative model to the data using a maximum likelihood optimization, enabling exact or approximate evaluations of sample likelihoods at the test time. Some popular choices include auto-regressive models (van den Oord et al., 2016b), (Oord et al., 2016), Variational Auto-encoders (Kingma & Welling, 2019), and methods based on normalizing 􀀍ow (Rezende & Mohamed, 2015) . Notably, 􀀍ow-based models (Kingma & Dhariwal, 2018; Dinh et al., 2017) leverage invertible generator functions to learn a bijective mapping between latent space and the data distribution, enabling an exact sample likelihood computation.  \nThe focus of this paper is to perform a comprehensive study of robustness of likelihood-based generative models to adversarial perturbations of their inputs. While there has been progress on adversarial robustness of classi􀀌cation problems (Madry et al. , 2018), robustness of likelihood models has not been explored in the literature. Performing such a sensitivity analysis is crucial for reliable deployment, especially in safetycritical applications. For instance, one application where likelihood estimation is crucial is unsupervised anomaly detection in medical imaging, where out-ofdistribution samples can be detected using likelihood scores. Adversarial attacks on such systems can lead to false diagnosis, potentially bearing life-threatening consequences.  \nFirst, we present a theoretical analysis of the sensitivity of linear generative models that 􀀌t a Gaussian  \n􀀃 First two authors contributed equally  \nClean image  \nIn-dis","cbCainUQVTOOIDul","https://ap.wps.com/l/cbCainUQVTOOIDul","pdf",2442840,"English","# Abstract\n# 1 Introduction\n## Generative modeling and likelihood-based approaches\n## Adversarial robustness motivation\n## Main theoretical and empirical contributions","[{\"question\":\"What problem does the paper study about flow-based generative models?\",\"answer\":\"It studies how flow-based generative models respond to adversarial perturbations, focusing on whether attackers can reduce likelihood scores for in-distribution and out-of-distribution inputs.\"},{\"question\":\"How do the authors analyze robustness in the paper?\",\"answer\":\"They provide a theoretical analysis for a linear flow-based generative model and then run empirical experiments on nonlinear flow models such as GLOW and RealNVP using designed adversarial attacks.\"},{\"question\":\"What defense approach improves robustness according to the paper?\",\"answer\":\"A hybrid adversarial training procedure significantly boosts robustness, reducing the impact of adversarial attacks on likelihood scores.\"}]","Adversarial Robustness of Flow-Based Generative Models - AISTATS 2020 Proceedings | PDF",25]