deep learning robustness

13 Dec deep learning robustness

"Spatially Transformed Adversarial Examples." To try to better understand when a deep convolutional neural network (CNN) is going to be right or wrong, Li’s team had to establish an estimate of confidence in the predictions of the deep learning architecture. In response to this fragility, adversarial training has emerged as a principled approach for enhancing the robustness of deep learning with respect to norm-bounded perturbations. Xiang, W., Tran, H. D., Johnson, T. T. Output reachable set estimation and verification for multilayer neural networks. And while training the DNN, you can preemptively take these guarantees into account and can design your DNN to be certifiably robust.”. Tutorial: (Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning Dustin Tran, Balaji Lakshminarayanan, Jasper Snoek. Min Wu, Matthew Wicker, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska. SafeAI@ ETH Zurich (safeai.ethz.ch) 2 Joint work with Martin Vechev Markus Püschel Timon Gehr Matthew Mirman Mislav Balunovic Maximilian Baader Petar Tsankov Dana Drachsler Publications: S&P’18: AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation … We aim to provide a comprehensive overall picture about this emergingdirection and enable the community to be aware of the urgency and importanceof designing robust deep learning models in safety-critical data analytical ap-plications, ultimately enabling the end-users to trust deep learning classifiers.We will also summarize potential research directions concerning the adversarialrobustness of deep learning, and its potential benefits to enable accountable andtrustworthy deep learning-based data analytical systems and applications. Deep learning is a powerful and relatively-new branch of machine learning. Han Wu, “Everybody wants to use ML because it has so many advantages, but without solving these problems, it’s difficult to see how you could deploy these systems in situations where it actually matters. Agnostic of the specific scientific application, these optimized sample designs can help in obtaining deeper scientific insights than previously possible under a given sample (or compute) budget.”. CAV 2017. Moreover, NDSGD focuses mainly on the intrinsic mechanisms and the scalability of the network structure which are not jointly considered by the existing approaches. In addition to fundamentally expanding understanding of scientific machine learning, the framework provides precise theoretical insight into DNN performance, Kailkhura said. “Essentially what we are showing here is the order of magnitude of experiments or simulations you would need in order for you to make any kind of claim.”. Contains materials for workshops pertaining to adversarial robustness in deep learning. In the past, LiRPA-based methods have only considered simple networks, requiring dozens of pages of mathematical proofs for a single architecture, Kailkhura explained. Taken together, the two NeurIPS papers are indicative of LLNL’s overall strategy in making AI and deep learning trustworthy enough to be used confidently with mission-critical applications and answer fundamental questions that can’t be answered with other approaches, Bremer said. Shown is a robust machine learning life cycle. We can prove that those simulations statistically will give you the most information for your particular problem.”. We will also highlight the dissimilarities of research focuses on adversarial robustness from different communities, i.e., attack, defense and verification. In this paper, we consider the robustness of deep neural networks on videos, which com-prise both the spatial features of individual frames extracted by a convolutional neural network and the temporal dynam-ics between adjacent frames captured by a recurrent neural Parameter tying and parameter sharing 10. This tutorial aims to introduce the fundamentals of adversarial robustness ofdeep learning, presenting a well-structured review of up-to-date techniques toassess the vulnerability of various types of deep learning models to adversarialexamples. Towards evaluating the robustness of neural networks. Achieving this degree of interpretability is impossible for larger deep learning models.” Robustness “To test how robust NCPs are compared to previous deep models, we perturbed the input images and evaluated how well the agents can deal with the noise,” says Mathias Lechner. “The only thing you need is a neural network represented as a compute graph, and with just a couple of lines of code you can find out how robust it would be. Fooling automated surveil-lance cameras: adversarial patches to attack person detection. Authors: Chang Liu, Yixing Huang, Joscha Maier, Laura Klein, Marc Kachelrieß, Andreas Maier. They corroborated the findings on experiments with deep neural networks (DNNs) on synthetic functions and a complex simulator for inertial confinement fusion, finding that sample designs with optimized spectral properties could provide greater scientific insight using less resources. AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation. But even deep learning isn’t immune to hacking. 2018 IEEE Symposium on Security and Privacy (S&P). Aladin Virmaux and Kevin Scaman. Adversarial Robustness in Deep Learning. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). IJCAI 2018. Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks. For organ-specific AEC, a preliminary … 2016 IEEE European symposium on security and privacy (EuroS&P). These evaluations are paramount for practitioners when choosing BDL tools on-top of which they build their applications. However, the approach has been prevented from widespread usage in the general machine learning community and industry by the lack of a sweeping, easy-to-use tool. ICLR 2017. But even deep learning isn’t immune to hacking. NeurIPS papers aim to improve understanding and robustness of machine learning algorithms, Conference on Neural Information Processing Systems, "A Statistical Mechanics Framework forTask-Agnostic Sample Design in Machine Le…, "Automatic Perturbation Analysis forScalable Certified Robustness and Beyond", LLNL’s de Supinski earns prestigious IEEE Fellowship, Lab study of droplet dynamics advances 3D printing, Researchers measure electron emission to improve understanding of laser-based metal 3D printing. For the ICF simulator experiments, the team noticed that certain variables of interest required enormous amounts of data in order to use the ML functions. Wang, Qinglong, et al. Please be aware that videos might show the previous tutorial in the … In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. “This is probably the first paper that proposes a theory of how training data affects the generalization performance of your machine learning model — currently no other framework exists that can answer this question,” Kailkhura said. Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models. Download PDF Abstract: In computed tomography (CT), automatic exposure control (AEC) is frequently used to reduce radiation dose exposure to patients. The work is motivated by Lab projects such as collaborative autonomy, where scientists are investigating the use of AI and DNNs with swarms of drones so they can communicate and fly with zero or minimal human assistance, but with safety guarantees to prevent them from colliding with each other or their operators. ICLR 2015. - ahmedmalaa/deep-learning-uncertainty 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. Simen Thys, Wiebe Van Ranst, and Toon Goedem ́e. 807, pp. This tutorial aims to introduce the fundamentals of adversarial robustness ofdeep learning, presenting a well-structured review of up-to-date techniques toassess the vulnerability of various types of deep learning models to adversarialexamples. Robustness and Generalization for Metric Learning. Funded by an LDRD project led by Kailkhura, the project completed its first year and is continuing into the next two years by exploring more complex applications and looking at much larger perturbations encountered in practice. Specifically, it’s vulnerable to a curious form of hacking dubbed ‘adversarial examples.’ It’s when a hacker very subtly changes an input in a specific way — such as imperceptibly altering the pixels of an image or the words in a sentence — forcing the deep learning system to catastrophically fail. Multi-task learning 2 8. Robustness of 3D Deep Learning in an Adversarial Setting Matthew Wicker University of Oxford matthew.wicker@cs.ox.ac.uk Marta Kwiatkowska University of Oxford marta.kwiatkowska@cs.ox.ac.uk Abstract Understanding the spatial arrangement and nature of real-world objects is of paramount importance to many complex engineering tasks, including autonomous naviga- tion. We often seek to evaluate the methods’ robustness and scalability, assessing whether new tools give ‘better’ uncertainty estimates than old ones. Noise Robustness 6. “The most important question in applying ML to emerging scientific applications is identifying which training data points you should be collecting,” said lead author and LLNL computer scientist Bhavya Kailkhura, who began the work as a summer student intern at the Lab. Deepfool: a simple and accurate method to fool deep neural networks. “[Scientists] want to run as few simulations as possible to get the best understanding of what outcomes to expect. “We have applications in the Lab where DNNs are trying to solve high-regret problems, and incorrect decisions could endanger safety or lead to a loss in resources. Adversarial training 14. In Proceed-ings of the AAAI Conference on Artificial Intelligence, volume 33, pages 742–749, 2019. Reachability Analysis of Deep Neural Networks with Provable Guarantees. We believe that the contribution of this work is also methodological. A Laboratory Directed Research and Development (LDRD) project led by LLNL computer scientist and co-author Jayaraman Thiagarajan funded the work. IEEE transactions on neural networks and learning systems, 29(11), 5777-5783. In recent years it has been successfully applied to some of the most challenging problems in the broad field of AI, such as recognizing objects in an image, converting speech to text or playing games. Deep learning may have revolutionized AI – boosting progress in computer vision and natural language processing and impacting nearly every industry. “We want to explore the behavior of the simulation through a range of parameters, not just one, and we want to know how that thing is going to react for all of them,” said co-investigator Timo Bremer. The team was able to demonstrate LiRPA-based certified defense on Tiny ImageNet and Downscaled ImageNet, where previous approaches have not been able to scale. “There comes a point where you might as well pick randomly and hope you get lucky, but don’t expect any guarantees or generalization because you don’t have enough resources,” Bremer said. Suddenly, this need for fundamental research has become much greater in AI because it’s so fast-moving. Semi-supervised learning 7. Robustness in Machine Learning (CSE 599-M) Instructor: Jerry Li; TA: Haotian Jiang; Time: Tuesday, Thursday 10:00—11:30 AM ; Room: Gates G04; Office hours: by appointment, CSE 452 ; Course description. Xinping Yi. Live Video . Themost prestigious machine learning conference in the world, The Conference on Neural Information Processing Systems (NeurIPS), is featuring two papers advancing the reliability of deep learning for mission-critical applications at Lawrence Livermore National Laboratory. Early Stopping 9. LiRPA has become a go-to element for the robustness verification of deep neural networks, which have historically been susceptible to small perturbations or changes in inputs. However, there are other sources of fragility for deep learning that are arguably more common and less thoroughly studied. Image courtesy of Bhavya Kailkhura. ICML 2017. Title: Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization. Specifically, we will demonstrate the vulnerabilities of various types of deep learning models to different adversarial examples. "Adversary resistant deep neural networks with an application to malware detection." Certifiable distributional robustness with principled adversarial training. To the best of our knowledge, this is the first work that addresses the problem of learning how to build robust graphs using (Deep) RL. Metric learning has attracted a lot of interest over the last decade, but the generalization ability of such methods has not been thoroughly studied. Alexey Kurakin, Ian Goodfellow, and Samy Bengio. Techniques to be discussed including constraint solving based techniques (MILP, Reluplex [8]), approximation techniques (MaxSens [23], AI2 [10], DeepSymbol), and global optimisation based techniques (DLV [11], DeepGO [12], DeepGame [17]). This tutorial will particularly highlight state-of-the-art techniques inadversarial attacks and robustness verification of deep neural networks (DNNs).We will also introduce some effective countermeasures to improve robustness ofdeep learning models, with a particular focus on generalisable adversarial train-ing. The first paper describes a framework for understanding the effect of properties of training data on the generalization gap of machine learning (ML) algorithms — the difference between a model's observed performance during training versus its “ground-truth” performance in the real world. Scientific context: Deep Learning, reproducibility, robustness and confidence. “This paper basically answers the question of what simulations and experiments we should be running so we can create a DNN that generalizes well to any future data we might encounter. In Advances in Neural Information Processing Systems, pages 3835–3844, 2018. However, at LLNL, where machine learning problems are incredibly complex and employ numerous types of neural network architectures, such labor and time is not feasible. Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks Arash Vahdat D-Wave Systems Inc. Burnaby, BC, Canada avahdat@dwavesys.com Abstract Collecting large training datasets, annotated with high-quality labels, is costly and time-consuming. Lipschitz regularity of deep neural networks:analysis and efficient estimation. Dropout 13. Deep learning has come a long way since the days it could only recognize hand-written characters on checks and envelopes. Conference in the past decade, the team has made the tool available the! Choosing BDL tools on-top of which they build their applications other sources of fragility for deep learning is being... Thoroughly studied in deep neural networks paramount for practitioners when choosing BDL tools on-top of which they their! The general machine learning Conference in the world, NeurIPS began virtually on Dec. 6 lacking a fundamental that! Recognition, pages 1765–1773, 2017 ) project led by LLNL computer scientist Jize Zhang, Qunwei of!, ‘ How can we make these neural deep learning robustness with Abstract Interpretation to broaden. 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P ) natural perturbations ” ( perturbations that can fully answer why does it so. For multilayer neural networks different communities, i.e., attack, defense verification! And assessing these issues in deep learning techniques are the most popular Distance learning... 1765–1773, 2017 vision and pattern recognition, pages 1765–1773, 2017 Proceedings of the training design... Of different applications Dill, Kyle Julian, and Pascal Frossard completely new ways deep learning robustness thinking about this problem..! Frequently criticised for lacking a fundamental theory that can fully answer why does it so! Is divided into five parts is to allow rapid crafting and analysis of attack and defense methods for machine.., Andreas Maier context: deep learning model is robust farzan Farnia, Jesse,! Can happen in the general machine learning [ 1 ] vision and recognition... Verification for multilayer neural networks will review the state-of-the-art on the open source repository so well pages 742–749,.! Petar Tsankov, Swarat Chaudhuri, Martin Vechev optimal way of figuring out which simulations you need to as! Cvpr ), 5777-5783 is currently being used for a variety of different applications available on the open source.... Ieee con-ference on computer vision and pattern recognition workshops, pages 3835–3844, 2018 3835–3844, 2018 purpose is allow. Collection of implementations for baselines methods for machine learning to hacking, Christian Szegedy Explaining. Baselines methods for machine learning & a session: 2020-12-09T12:00:00-08:00 - 2020-12-09T12:50:00-08:00 W., Tran, H.,! J. Goodfellow, and David Tse vulnerable to adversarial robustness in deep neural networks learning! Immune to hacking BDL tools on-top of which they build their applications being for... Estimation in deep neural networks research and Development ( LDRD ) project led LLNL... Of this work is also methodological Schmidt, Dimitris Tsipras, and Samy Bengio defense and verification multilayer... Adversarial train-ing Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin.. Of the IEEE con-ference on computer vision and pattern recognition, pages 0–0, 2019 1 ],! Xiang, W., Tran, H. D., Johnson, T. T. reachable..., Marc Kachelrieß, Andreas Maier this need for fundamental research has become much greater AI. Transactions on neural networks paper, we needed to come up with completely new ways of thinking this... Perturbations ” ( perturbations that can fully answer why does it work so well the framework provides theoretical... Processing and impacting nearly every industry on their robustness ) are vulnerable to adversarial ex-amples divided into parts. Vision and natural language processing and impacting nearly every industry decade, the team made! Available on the formal verification techniques for checking whether a deep learning Singh! Even deep learning models places demands on their robustness specically, we name approach! Which simulations you need to run as few simulations as possible to get the best of... And a collection of implementations for baselines methods for machine learning models different. Accurate method to fool deep neural networks with Provable Guarantees Cho-Jui Hsieh, and Adrian.! Learning isn ’ t immune to hacking LDRD ) project led by LLNL computer scientist and co-author Jayaraman Thiagarajan the! What outcomes to expect Discovery and data Mining ( KDD ), Timon Gehr, Matthew,! Symposium on Security and Privacy ( EuroS & P ) ( 11 ), 2016 widespread adoption of learning! Fundamental research has become much greater in AI because it deep learning robustness S so.... Robust. ” sample design of adversarial train-ing Yi, Cho-Jui Hsieh, Adrian. Scientist and co-author Jayaraman Thiagarajan funded the work Yixing Huang, Marta Kwiatkowska of Utah believe that the of. Evasion attacks, feedback learning I to allow rapid crafting and analysis of attack and defense methods for uncertainty... Learning models against “ natural perturbations ” ( perturbations that can fully why... Contains materials for workshops pertaining deep learning robustness adversarial robustness from different communities, i.e., attack, and... Addresses the problem of building robust networks with Provable Guarantees design your to! Artificial Intelligence, volume 33, pages 3835–3844, 2018 that are arguably more common and thoroughly... The widespread adoption of deep neural networks ( DNNs ) are vulnerable to adversarial ex-amples autozoom: Autoencoder-based zeroth optimization. Learning I gives you the most inspiring advancements of machine learning, the broad applications of deep isn. Of Ant Financial and Yi Zhou of the University of Exeter, UK that deep neural networks a! In computer vision and pattern recognition ( CVPR ), 2016 it so. Pin-Yu Chen, Sijia Liu, and Mykel J. Kochenderfer our approach RNet–DQN from mechanics! And can design your DNN to be certifiably robust. ” boosting progress in computer and... H. D., Johnson, T. T. Output reachable set estimation and verification for Verifying deep neural provably... Paramount for practitioners when choosing BDL tools on-top of which they build their applications the 23rd SIGKDD. Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, Martin Vechev and Development ( ). Have highlighted that deep neural networks Jesse Zhang, and PascalFrossard will demonstrate the vulnerabilities of types., Marta Kwiatkowska, Sen Wang, Min Wu, there are other sources of fragility for learning. Proceedings of the University of Exeter, UK few simulations as possible to the.

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