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December 1, 2020

endobj Authors: Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato. 11 0 obj >> Creating human understandable adversarial examples (as in Szegedy et al.) stream 3 0 obj What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? Data augmentation is also data transformation but it is used so as to have more data and to train a robust model. NeurIPS 2019. /Annots [ 570 0 R 571 0 R 572 0 R 573 0 R 574 0 R 575 0 R 576 0 R 577 0 R 578 0 R 579 0 R 580 0 R 581 0 R 582 0 R 583 0 R ] >> Robustness to Adversarial Perturbations in Learning from Incomplete Data. << Introduction to adversarial robustness: this part will introduce the concept of adversarial robustness by showing some examples from computer vision, natural language processing, and malware detection, autonomous systems. /Type /Page >> As a matter of fact, adversarial networks deceive into reconstructing things that aren’t part of the data. Machine Learning / Deep Learning. 10 0 obj In this blog post, we explain how our work in learning perturbation sets can bridge the gap between $\ell_p$ adversarial defenses and adversarial robustness to real-world transformations. /Contents 425 0 R 12 0 obj Adversarial Robustness Against the Union of Multiple Perturbation Models Algorithm 1 Multi steepest descent for learning classiﬁers that are simultaneously robust to ℓp attacks for p ∈ S Input: classiﬁer fθ, data x, labels y Parameters: ǫp,αp for p ∈ S, maximum iterations T, loss function ℓ /Parent 1 0 R /Annots [ 35 0 R 36 0 R 37 0 R 38 0 R 39 0 R 40 0 R 41 0 R 42 0 R 43 0 R 44 0 R 45 0 R 46 0 R 47 0 R 48 0 R 49 0 R 50 0 R ] Deep learning is progressing at an astounding rate with a wide range of real-world applications, such as computer vision , speech recognition and natural language processing .Despite these successful applications, the emergence of adversarial examples , , images containing perturbations imperceptible to human but misleading to DNNs, poses potential security threats to … /Resources 174 0 R /Parent 1 0 R /Group 568 0 R In this blog post, we want to share our high-level perspective on this phenomenon and how it fits into a larger question of robustness in machine learning. /lastpage (5551) To date, most existing adversarial perturbations are designed to attack CNN image classiﬁers, e.g., [4,6,10,14,15,16,19,23]. Now, we show that this procedure generalizes, … << /Type /Catalog /Resources 585 0 R Title: Robustness to Adversarial Perturbations in Learning from Incomplete Data. /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R 14 0 R ] When implemented with deep neural networks, our method shows a comparable performance to those of the state-of-the-art on a number of real-world benchmark datasets.

, Do not remove: This comment is monitored to verify that the site is working properly, Advances in Neural Information Processing Systems 32 (NeurIPS 2019). endobj /MediaBox [ 0 0 612 792 ] To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. /MediaBox [ 0 0 612 792 ] ;�. According to the researchers, modifying the training strategy can optimise the security and robustness of models. To provide a concrete answer to this question, this paper unifies two major learning frameworks: Semi-Supervised Learning (SSL) and Distributionally Robust Learning (DRL). << Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness Adversarial Texture Optimization From RGB-D Scans Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory >> << In particular, it is expected that a robust classiﬁer be /Contents 251 0 R endobj /EventType (Poster) /Type /Page /Type /Page /Contents 173 0 R /Contents 211 0 R >> << /Parent 1 0 R Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najaﬁ Department of Computer Engineering Sharif University of Technology Tehran, Iran [email protected] Shin-ichi Maeda Preferred Networks, Inc. Tokyo, Japan [email protected] Masanori Koyama Preferred Networks, Inc. Tokyo, Japan [email protected] Takeru Miyato /Annots [ 240 0 R 241 0 R 242 0 R 243 0 R 244 0 R 245 0 R 246 0 R 247 0 R 248 0 R 249 0 R 250 0 R ] 2.2 Distributionally Robust Optimization Distributionally Robust Optimization (DRO) seeks to optimize in the face of a stronger adversary. Most works focus the robustness of classiﬁers under ℓp-norm bounded perturba-tions. /Type /Page /MediaBox [ 0 0 612 792 ] The goal of RobustBench is to systematically track the real progress in adversarial robustness. 2 0 obj robustness of classiﬁers to adversarial perturbations, and then illustrate and specialize the obtained upper bound for the families of linear and quadratic classiﬁers. endobj Over the past few years, adversarial examples have received a significant amount of attention in the deep learning community. ifold learning [37, 29], data transformation and compression [40, 15], statistical analysis [44], and regularization [43]. However, existing adversarial perturbations can impact accuracy as well as the quality of image reconstruction. /Filter /FlateDecode /Language (en\055US) /Contents 588 0 R >> /Contents 132 0 R 3 Robustness Certiﬁcate From results in the previous section, Algorithm 1 provably learns to protect against adversarial perturbations on the training dataset. You can find more details in. x��ZM�۶��Бz�K��qj7Mj�nv����-�2Iy���}3��! /Parent 1 0 R │ ├── Robust Graph Learning From Noisy Data.pdf │ ├── Robust Spammer Detection by Nash Reinforcement Learning.pdf │ ├── Robust Training of Graph Convolutional Networks via Latent Perturbation.pdf │ ├── Tensor Graph Convolutional Networks for Multi-relational and Robust Learning… /Annots [ 101 0 R 102 0 R 103 0 R 104 0 R 105 0 R 106 0 R 107 0 R 108 0 R 109 0 R 110 0 R 111 0 R 112 0 R 113 0 R 114 0 R 115 0 R 116 0 R 117 0 R 118 0 R 119 0 R 120 0 R 121 0 R 122 0 R 123 0 R 124 0 R 125 0 R 126 0 R 127 0 R 128 0 R 129 0 R 130 0 R 131 0 R ] /Parent 1 0 R 9 0 obj /Resources 587 0 R ceptible perturbations to data can lead to misbehavior of the model, such as misclassiﬁcation of the ... defense against imperceptible adversarial perturbations—are achievable at essentially no computa- ... stantial body of work on robustness and learning. /Resources 235 0 R /Annots [ 403 0 R 404 0 R 405 0 R 406 0 R 407 0 R 408 0 R 409 0 R 410 0 R 411 0 R 412 0 R 413 0 R 414 0 R 415 0 R 416 0 R 417 0 R 418 0 R 419 0 R 420 0 R 421 0 R 422 0 R 423 0 R 424 0 R ] An adversarial e x ample is an input designed to fool a machine learning model [1]. 15 0 obj /firstpage (5541) /Published (2019) Training (AT). /Contents 586 0 R Runtime Masking and Cleansing In this section, we present the runtime masking and cleans-ing (RMC). << /MediaBox [ 0 0 612 792 ] Adversarial training techniques for single modal tasks on images and text have been shown to make a model more robust and generalizable. /Annots [ 213 0 R 214 0 R 215 0 R 216 0 R 217 0 R 218 0 R 219 0 R 220 0 R 221 0 R 222 0 R 223 0 R 224 0 R 225 0 R 226 0 R 227 0 R 228 0 R 229 0 R 230 0 R 231 0 R 232 0 R 233 0 R ] /Editors (H\056 Wallach and H\056 Larochelle and A\056 Beygelzimer and F\056 d\047Alch\351\055Buc and E\056 Fox and R\056 Garnett) /Resources 589 0 R >> /Date (2019) Adding adversarial perturbations to the embedding space (as in FreeLB). /Type /Page << Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Naja Shin-ichi Maeda yMasanori Koyama Takeru Miyato * Computer Engineering Department Sharif University of Technology, Tehran, Iran yPreferred Networks Inc., Tokyo, Japan Abstract What is the role of unlabeled data in an inference problem, when the presumed underlying /Parent 1 0 R ... (ǫ,δ)p robust to adversarial perturbations over the set X, if Neural networks are very susceptible to adversarial examples, a.k.a., small perturbations of normal inputs that cause a classifier to output the wrong label. Using our previous example, a requirement specification might detail the expected behavior of a machine learning model against adversarial perturbations or a given set of safety constraints. Download post as jupyter notebook. 1 0 obj /Parent 1 0 R %PDF-1.3 << This sort of training can be done by. By : Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato. >> Download PDF Abstract: What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? /Parent 1 0 R >> << /Resources 212 0 R Writing robust machine learning programs is a combination of many aspects ranging from accurate training dataset to efficient optimization techniques. Part of: Advances in Neural ... (SSL) and Distributionally Robust Learning (DRL). Moreover, our analysis is able to quantify the role of unlabeled data in the generalization under a more general condition compared to the existing theoretical works in SSL. /Annots [ 138 0 R 139 0 R 140 0 R 141 0 R 142 0 R 143 0 R 144 0 R 145 0 R 146 0 R 147 0 R 148 0 R 149 0 R 150 0 R 151 0 R 152 0 R 153 0 R 154 0 R 155 0 R 156 0 R 157 0 R 158 0 R 159 0 R 160 0 R 161 0 R 162 0 R 163 0 R 164 0 R 165 0 R 166 0 R 167 0 R 168 0 R 169 0 R 170 0 R 171 0 R 172 0 R ] vulnerable to adversarial perturbations, which are intentionally crafted noises that are im-perceptible to human observer, but can lead to large errors in the deep network models when added to images. Area. Robustness to Adversarial Perturbations in Learning from Incomplete Data December 2019 Conference: Advances in Neural Information Processing Systems (NeurIPS 2019) Introduction. distributionally robust optimization tractable for deep learning. /MediaBox [ 0 0 612 792 ] Response Summary: The demonstration of models that learn from high-frequency components of the data is interesting and nicely aligns with our findings.Now, even though susceptibility to noise could indeed arise from non-robust useful features, this kind of brittleness (akin to adversarial examples) of ML models has been so far predominantly viewed as a consequence of model “bugs” … >> Robustness to ℓp-norm perturbations. What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? /Type /Pages However, the majority of the defense schemes in the litera-ture are compromised by more sophisticated attacks [7, 6]. To provide a concrete answer to this question, this paper unifies two major learning frameworks: Semi-Supervised Learning (SSL) and Distributionally Robust Learning (DRL). 14 0 obj 8 0 obj /MediaBox [ 0 0 612 792 ] 4 0 obj /Length 3242 ∙ Sharif Accelerator ∙ Preferred Infrastructure ∙ 0 ∙ share What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? endobj >> << 7 0 obj Although many notions of robustness and reliability exist, one particular topic in this area that has raised a great deal of interest in recent years is that of adversarial robustness: can we develop … /Contents 15 0 R /Contents 234 0 R /Contents 51 0 R << 3. >> endobj >> >> endobj /Type /Page /MediaBox [ 0 0 612 792 ] /Description-Abstract (What is the role of unlabeled data in an inference problem\054 when the presumed underlying distribution is adversarially perturbed\077 To provide a concrete answer to this question\054 this paper unifies two major learning frameworks\072 Semi\055Supervised Learning \050SSL\051 and Distributionally Robust Learning \050DRL\051\056 We develop a generalization theory for our framework based on a number of novel complexity measures\054 such as an adversarial extension of Rademacher complexity and its semi\055supervised analogue\056 Moreover\054 our analysis is able to quantify the role of unlabeled data in the generalization under a more general condition compared to the existing theoretical works in SSL\056 Based on our framework\054 we also present a hybrid of DRL and EM algorithms that has a guaranteed convergence rate\056 When implemented with deep neural networks\054 our method shows a comparable performance to those of the state\055of\055the\055art on a number of real\055world benchmark datasets\056) /ModDate (D\07220200213000529\05508\04700\047) /Pages 1 0 R /Type /Page In general, there are two broad branches in adversarial machine learning, i.e., certified robust training [35, 30, 8, 14] and empirical robust training [17, 36, 33]. We develop a generalization theory for our framework based on a number of novel complexity measures, such as an adversarial extension of Rademacher complexity and its semi-supervised analogue. /Producer (PyPDF2) endobj Model Compression with Adversarial Robustness: A Unified Optimization Framework; Robustness to Adversarial Perturbations in Learning from Incomplete Data; Adversarial Training and Robustness for Multiple Perturbations; On the Hardness of Robust Classification; Theoretical evidence for adversarial robustness through randomization endobj As we seek to deploy machine learning systems not only on virtual domains, but also in real systems, it becomes critical that we examine not only whether the systems don’t simply work “most of the time”, but which are truly robust and reliable. endobj 6 0 obj /Count 11 The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. Robustness to Adversarial Perturbations in Learning from Incomplete Data. 1. << An adversarial example crafted as a change to a benign input is known as an adversarial perturbation. /Book (Advances in Neural Information Processing Systems 32) /Type (Conference Proceedings) Robustness to Adversarial Perturbations in Learning from Incomplete Data. Based on our framework, we also present a hybrid of DRL and EM algorithms that has a guaranteed convergence rate. The standard defense against adversarial examples is Adversarial Training, which trains a classifier using adversarial examples close to training inputs. Learning the parameters via AT yields robust models in practice, but it is not clear to what extent robustness will generalize to adversarial perturbations of a held-out test set. /MediaBox [ 0 0 612 792 ] endobj Recently, many efforts have been made on learning robust DNNs to resist such adversarial examples. /Resources 52 0 R /Publisher (Curran Associates\054 Inc\056) /Resources 133 0 R In both cases, our results show the existence of a fundamental limit on the robustness to adversarial perturba-tions. /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) Generating pixel-level adversarial perturbations has been and remains exten-sively studied [16, 18–20, 27, 28]. /Parent 1 0 R There are already more than 2'000 papers on this topic, but it is still unclear which approaches really work and which only lead to overestimated robustness.We start from benchmarking the $$\ell_\infty$$- and $$\ell_2$$-robustness since these are the most studied settings in the literature. /MediaBox [ 0 0 612 792 ] (2) Adversarial training, even with an empirical perturbation algorithm such as FGM, can in fact be provably robust against ANY perturbations of the same radius. /Contents 584 0 R /Type /Page /Type /Page << endobj /Resources 426 0 R /MediaBox [ 0 0 612 792 ] labeled data to better learn the underlying data distribution or the relationship between data points and labels, our goal is to use unlabeled data to unlearn patterns that are harmful to adversarial robustness (i.e., to cleanse the model). /Group 401 0 R /Resources 252 0 R /Author (Amir Najafi\054 Shin\055ichi Maeda\054 Masanori Koyama\054 Takeru Miyato) /Parent 1 0 R Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019), Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato,

What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? Adversarial robustness has been initially studied solely through the lens of machine learning security, but recently a line of work studied the effect of imposing adversarial robustness as a prior on learned feature representations. /Type /Page >> 05/24/2019 ∙ by Amir Najafi, et al. endobj /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) /Resources 16 0 R /Created (2019) Our paper on arXiv here: [Wong & Kolter, 2020] Our code repository here: Table of contents 5 0 obj 13 0 obj /MediaBox [ 0 0 612 792 ] /Parent 1 0 R (1) Training over the original data is indeed non-robust to small adversarial perturbations of some radius. /Title (Robustness to Adversarial Perturbations in Learning from Incomplete Data) << /Annots [ 183 0 R 184 0 R 185 0 R 186 0 R 187 0 R 188 0 R 189 0 R 190 0 R 191 0 R 192 0 R 193 0 R 194 0 R 195 0 R 196 0 R 197 0 R 198 0 R 199 0 R 200 0 R 201 0 R 202 0 R 203 0 R 204 0 R 205 0 R 206 0 R 207 0 R 208 0 R 209 0 R 210 0 R ] << /Type /Page endobj