<< 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. Generating pixel-level adversarial perturbations has been and remains exten-sively studied [16, 18–20, 27, 28]. Introduction. /Type /Page 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. /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 ] /Parent 1 0 R endobj Writing robust machine learning programs is a combination of many aspects ranging from accurate training dataset to efficient optimization techniques. >> /Parent 1 0 R /Type /Pages 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. 2 0 obj Title: Robustness to Adversarial Perturbations in Learning from Incomplete Data. 13 0 obj >> >> /Resources 252 0 R Adding adversarial perturbations to the embedding space (as in FreeLB). >> As a matter of fact, adversarial networks deceive into reconstructing things that aren’t part of the data. /MediaBox [ 0 0 612 792 ] /Resources 52 0 R In both cases, our results show the existence of a fundamental limit on the robustness to adversarial perturba-tions. /MediaBox [ 0 0 612 792 ] Robustness to Adversarial Perturbations in Learning from Incomplete Data. However, existing adversarial perturbations can impact accuracy as well as the quality of image reconstruction. /MediaBox [ 0 0 612 792 ] Area. This sort of training can be done by. /Resources 16 0 R /Resources 587 0 R You can find more details in. distributionally robust optimization tractable for deep learning. stream 5 0 obj What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najaﬁ Department of Computer Engineering Sharif University of Technology Tehran, Iran najafy@ce.sharif.edu Shin-ichi Maeda Preferred Networks, Inc. Tokyo, Japan ichi@preferred.jp Masanori Koyama Preferred Networks, Inc. Tokyo, Japan masomatics@preferred.jp Takeru Miyato ;�. endobj 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. 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 To provide a concrete answer to this question, this paper unifies two major learning frameworks: Semi-Supervised Learning (SSL) and Distributionally Robust Learning (DRL). /Type /Page /Group 568 0 R 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” … >> 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. An adversarial example crafted as a change to a benign input is known as an adversarial perturbation. 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 Training (AT). 3 0 obj 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. /Contents 173 0 R /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 ] 3. /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) >> 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. Recently, many efforts have been made on learning robust DNNs to resist such adversarial examples. >> /Parent 1 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. 10 0 obj >> << 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]. /Parent 1 0 R ∙ 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? /Parent 1 0 R /MediaBox [ 0 0 612 792 ] Our paper on arXiv here: [Wong & Kolter, 2020] Our code repository here: Table of contents Over the past few years, adversarial examples have received a significant amount of attention in the deep learning community. endobj 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). ... (ǫ,δ)p robust to adversarial perturbations over the set X, if endobj /Type /Page /firstpage (5541) /Resources 133 0 R ifold learning [37, 29], data transformation and compression [40, 15], statistical analysis [44], and regularization [43]. (1) Training over the original data is indeed non-robust to small adversarial perturbations of some radius. << An adversarial e x ample is an input designed to fool a machine learning model [1]. /Type /Catalog << /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) 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 >> /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 ] 7 0 obj << /MediaBox [ 0 0 612 792 ] Download post as jupyter notebook. 1 0 obj /Resources 212 0 R endobj Data augmentation is also data transformation but it is used so as to have more data and to train a robust model. << >> 12 0 obj Adversarial training techniques for single modal tasks on images and text have been shown to make a model more robust and generalizable. endobj Robustness to ℓp-norm perturbations. 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 /MediaBox [ 0 0 612 792 ] Authors: Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato. /Parent 1 0 R 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). /Type /Page /Resources 235 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 ] << /Author (Amir Najafi\054 Shin\055ichi Maeda\054 Masanori Koyama\054 Takeru Miyato) %PDF-1.3 /Type /Page /Contents 211 0 R /Parent 1 0 R /Contents 584 0 R By : Amir Najafi, Shin-ichi Maeda, Masanori Koyama, Takeru Miyato. endobj /Parent 1 0 R /Type /Page /Resources 585 0 R endobj /Contents 425 0 R /Parent 1 0 R What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? /Title (Robustness to Adversarial Perturbations in Learning from Incomplete Data) /Type /Page >> 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. 9 0 obj 15 0 obj << endobj /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 ] << 6 0 obj << /Publisher (Curran Associates\054 Inc\056) 4 0 obj 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. /MediaBox [ 0 0 612 792 ] /Resources 589 0 R 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. Robustness to Adversarial Perturbations in Learning from Incomplete Data December 2019 Conference: Advances in Neural Information Processing Systems (NeurIPS 2019) 2.2 Distributionally Robust Optimization Distributionally Robust Optimization (DRO) seeks to optimize in the face of a stronger adversary. /Language (en\055US) /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) /Contents 586 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 … /Type /Page Most works focus the robustness of classiﬁers under ℓp-norm bounded perturba-tions. The standard defense against adversarial examples is Adversarial Training, which trains a classifier using adversarial examples close to training inputs. x��ZM�۶��Бz�K��qj7Mj�nv����-�2Iy���}3��! 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. /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) robustness of classiﬁers to adversarial perturbations, and then illustrate and specialize the obtained upper bound for the families of linear and quadratic classiﬁers. Machine Learning / Deep Learning. │ ├── 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…