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Attacking Speaker Recognition With Deep Generative Models. Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning. or made inconsistent Information Aware Max-Norm Dirichlet Networks for Predictive Uncertainty Estimation. false positives on the most recent few entries. Provable trade-offs between private & robust machine learning. Robustness Verification of Tree-based Models. UnMask: Adversarial Detection and Defense Through Robust Feature Alignment. Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals. Attacks Which Do Not Kill Training Make Adversarial Learning Stronger. CAAD 2018: Iterative Ensemble Adversarial Attack. Geometric robustness of deep networks: analysis and improvement. Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations. Enhanced Attacks on Defensively Distilled Deep Neural Networks. Transferable Adversarial Robustness using Adversarially Trained Autoencoders. Brain-inspired reverse adversarial examples. Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering. Robustness of 3D Deep Learning in an Adversarial Setting. (15%), DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment. APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection. Towards Evaluating the Robustness of Chinese BERT Classifiers. Defensive Distillation is Not Robust to Adversarial Examples. Training Augmentation with Adversarial Examples for Robust Speech Recognition. Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks. DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars. Not My Deepfake: Towards Plausible Deniability for Machine-Generated Media. CodNN -- Robust Neural Networks From Coded Classification. Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations. Multi-Step Adversarial Perturbations on Recommender Systems Embeddings. Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients. Learning to fool the speaker recognition. We present the adversarial attacks and defenses problem as an infinite zero-sum game where classical results do not apply. TextAttack: Lessons learned in designing Python frameworks for NLP. Adversarial attacks against Fact Extraction and VERification. From Hero to Z\'eroe: A Benchmark of Low-Level Adversarial Attacks. Detecting and Correcting Adversarial Images Using Image Processing Operations and Convolutional Neural Networks. Yet, for adversarial examples this correlation should break and thus, it will serve as an Flow-based detection and proxy-based evasion of encrypted malware C2 traffic. Perturbation Analysis of Gradient-based Adversarial Attacks. Analyzing the Noise Robustness of Deep Neural Networks. Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior. On Training Robust PDF Malware Classifiers. Rademacher Complexity for Adversarially Robust Generalization. Intermediate Level Adversarial Attack for Enhanced Transferability. A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles. An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs. Query-Efficient Black-box Adversarial Examples (superceded). SOCRATES: Towards a Unified Platform for Neural Network Verification. Neural Network Robustness Verification on GPUs. Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. Houdini: Fooling Deep Structured Prediction Models. Intriguing properties of neural networks. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models. A Comparative Study of Rule Extraction for Recurrent Neural Networks. A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples. CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition. A Study of the Transformation-based Ensemble Defence. An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks. worth reading, see the Source Adversarial Risk Bounds via Function Transformation. (1%), Reinforcement Based Learning on Classification Task Could Yield Better Generalization and Adversarial Accuracy. Robustness Guarantees for Deep Neural Networks on Videos. Spatiotemporal Attacks for Embodied Agents. (56%), ONION: A Simple and Effective Defense Against Textual Backdoor Attacks. Towards Robust Deep Learning with Ensemble Networks and Noisy Layers. Data Augmentation via Structured Adversarial Perturbations. Adversarial Defense by Stratified Convolutional Sparse Coding. Interpreting and Evaluating Neural Network Robustness. Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems. Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation. Boosting Adversarial Training with Hypersphere Embedding. Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model. Adversarial Attacks on Monocular Depth Estimation. The Search for Sparse, Robust Neural Networks. Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers. Examining Adversarial Learning against Graph-based IoT Malware Detection Systems. Fast & Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization. Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods. Countering Inconsistent Labelling by Google's Vision API for Rotated Images. Analyzing the Interpretability Robustness of Self-Explaining Models. Therefore, adversarial examples pose a security problem for all downstream systems that include neural networks, including text-to-speech systems and self-driving cars. TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks. Transferable Universal Adversarial Perturbations Using Generative Models. Crafting Adversarial Input Sequences for Recurrent Neural Networks. Object Hider: Adversarial Patch Attack Against Object Detectors. Addressing Color Constancy Errors on Deep Neural Network Performance. Improving the Adversarial Robustness of Transfer Learning via Noisy Feature Distillation. Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. RNN-Test: Adversarial Testing Framework for Recurrent Neural Network Systems. Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning. A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. L 1-norm double backpropagation adversarial defense. RayS: A Ray Searching Method for Hard-label Adversarial Attack. MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks. Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models. Evaluations and Methods for Explanation through Robustness Analysis. Accurate and Robust Feature Importance Estimation under Distribution Shifts. Natural and Adversarial Error Detection using Invariance to Image Transformations. One pixel attack for fooling deep neural networks. Adversarial examples have attracted significant attention in machine learning, but the reasons for their existence and pervasiveness remain unclear. A Method for Computing Class-wise Universal Adversarial Perturbations. AI-Powered GUI Attack and Its Defensive Methods. Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection. Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction. Towards the Science of Security and Privacy in Machine Learning. Politics of Adversarial Machine Learning. A study of the effect of JPG compression on adversarial images. Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images. An Alternative Surrogate Loss for PGD-based Adversarial Testing. Adversarial Reprogramming of Text Classification Neural Networks. (1%), Practical No-box Adversarial Attacks against DNNs. paper “Adversarial examples are not bugs, they are features”. CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks. Early attempts at explaining this phenomenon focused on nonlinearity … ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples. Investigating Decision Boundaries of Trained Neural Networks. these criteria (and are about something different instead), Residual Networks as Nonlinear Systems: Stability Analysis using Linearization. (99%), Contextual Fusion For Adversarial Robustness. MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius. HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples. We identify quantities from generalization analysis of NNs; with the identifed quantities we empirically fnd that AR is achieved by regularizing/biasing NNs towards less confdent solutions by making the changes in the feature space (induced by changes in the instance space) of most layers smoother uniformly in all directions; so to a certain extent, it prevents sudden change in prediction w.r t. perturbations. AdvJND: Generating Adversarial Examples with Just Noticeable Difference. (99%), Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks. Adversarial Samples on Android Malware Detection Systems for IoT Systems. Building Robust Deep Neural Networks for Road Sign Detection. Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels. A Black-Box Attack Model for Visually-Aware Recommender Systems. Adversarial Robustness of Flow-Based Generative Models. Purifying Adversarial Perturbation with Adversarially Trained Auto-encoders. Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API. Several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning. Efficient Defenses Against Adversarial Attacks. Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain. Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences. Comment on "Biologically inspired protection of deep networks from adversarial attacks". Adversarial Risk Bounds for Neural Networks through Sparsity based Compression. MalFox: Camouflaged Adversarial Malware Example Generation Based on C-GANs Against Black-Box Detectors. Smoothed Inference for Adversarially-Trained Models. Robust binary classification with the 01 loss. GeoDA: a geometric framework for black-box adversarial attacks. Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks. Improved Gradient based Adversarial Attacks for Quantized Networks. Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming. Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples. (99%), Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression. Protecting JPEG Images Against Adversarial Attacks. A Statistical Defense Approach for Detecting Adversarial Examples. Adversarial Robustness May Be at Odds With Simplicity. Automated Testing for Deep Learning Systems with Differential Behavior Criteria. Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference. It is trivial to perform adversarial attack by adding excessive noises, but currently there is no refinement mechanism to squeeze redundant noises. Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness. Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness. Manifold Mixup: Better Representations by Interpolating Hidden States. (99%), Detecting Universal Trigger's Adversarial Attack with Honeypot. Patch-wise Attack for Fooling Deep Neural Network. Now that we’ve seen how adversarial examples and robust optimization work in the context of linear models, let’s move to the setting we really care about: the possibility of adversarial examples in deep neural networks. Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria. Sparse PCA: Algorithms, Adversarial Perturbations and Certificates. $n$-ML: Mitigating Adversarial Examples via Ensembles of Topologically Manipulated Classifiers. Exploring and Improving Robustness of Multi Task Deep Neural Networks via Domain Agnostic Defenses. Exploring the Hyperparameter Landscape of Adversarial Robustness. Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations. Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming. Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables. Towards adversarial robustness with 01 loss neural networks. Adversarial Example Detection by Classification for Deep Speech Recognition. Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning. Guiding Deep Learning System Testing using Surprise Adequacy. Defense against adversarial attacks on spoofing countermeasures of ASV. Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. Robust Neural Networks using Randomized Adversarial Training. Quantifying Perceptual Distortion of Adversarial Examples. Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment. Adversarial attacks hidden in plain sight. Robust Deep Learning Ensemble against Deception. Generating Adversarial Examples With Conditional Generative Adversarial Net. Universalization of any adversarial attack using very few test examples. Semidefinite relaxations for certifying robustness to adversarial examples. A general metric for identifying adversarial images. Colored Noise Injection for Training Adversarially Robust Neural Networks. Adversary Detection in Neural Networks via Persistent Homology. Bounding The Number of Linear Regions in Local Area for Neural Networks with ReLU Activations. Did you hear that? Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables. Adversarial Noise Layer: Regularize Neural Network By Adding Noise. Proper Network Interpretability Helps Adversarial Robustness in Classification. Evading Deepfake-Image Detectors with White- and Black-Box Attacks. Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions. Heat and Blur: An Effective and Fast Defense Against Adversarial Examples. Adversary A3C for Robust Reinforcement Learning. Vulnerabilities of Connectionist AI Applications: Evaluation and Defence. Black-box Adversarial Attacks with Bayesian Optimization. (99%), Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers. c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation. Deep Neural Rejection against Adversarial Examples. Randomization matters. On the Vulnerability of CNN Classifiers in EEG-Based BCIs. An Explainable Adversarial Robustness Metric for Deep Learning Neural Networks. (99%), Adversarial Threats to DeepFake Detection: A Practical Perspective. Moreover, our analysis demonstrates that, in the initial phase of adversarial training, the scale of the inputs matters in the sense that a smaller input scale leads to faster convergence of adversarial training and a “more regular” landscape. adversarial examples. Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks. Attack Agnostic Statistical Method for Adversarial Detection. Defending Your Voice: Adversarial Attack on Voice Conversion. MixTrain: Scalable Training of Verifiably Robust Neural Networks. Universal Adversarial Audio Perturbations. Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text Attacks. Siamese Generative Adversarial Privatizer for Biometric Data. Fast Gradient Attack on Network Embedding. It encouraged researchers to develop query-efficient adversarial attacks that can successfully operate against a wide range of defenses while just observing the final model decision to generate adversarial examples. QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks. Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks. HyperNetworks with statistical filtering for defending adversarial examples. CALPA-NET: Channel-pruning-assisted Deep Residual Network for Steganalysis of Digital Images. Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples. A Fourier Perspective on Model Robustness in Computer Vision. T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack. In this paper, we explore the landscape around adversarial training in a … Feature-level Malware Obfuscation in Deep Learning. Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification. On the Effectiveness of Low Frequency Perturbations. Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. Deep Q learning for fooling neural networks. Learning to Disentangle Robust and Vulnerable Features for Adversarial Detection. On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm. Provable tradeoffs in adversarially robust classification. Compared to lpl_plp​ norm metric, Wasserstein distance, which takes geometry in pixel space into account, has long known to be a better metric for measuring image quality and has recently risen as a compelling alternative to the lpl_plp​ metric in adversarial attacks. A Training-based Identification Approach to VIN Adversarial Examples. Disentangled Deep Autoencoding Regularization for Robust Image Classification. Dynamically Computing Adversarial Perturbations for Recurrent Neural Networks. Does Network Width Really Help Adversarial Robustness? AdvMind: Inferring Adversary Intent of Black-Box Attacks. Generating Adversarial Examples withControllable Non-transferability. Improving Robustness of Deep-Learning-Based Image Reconstruction. Towards Robust Sensor Fusion in Visual Perception. Say What I Want: Towards the Dark Side of Neural Dialogue Models. Generalizing Universal Adversarial Attacks Beyond Additive Perturbations. Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection. Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior. A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome. Few-Features Attack to Fool Machine Learning Models through Mask-Based GAN. CopyCAT: Taking Control of Neural Policies with Constant Attacks. Large-Scale Adversarial Training for Vision-and-Language Representation Learning. An Adversarial Attack Defending System for Securing In-Vehicle Networks. Intriguing properties of adversarial training. Black-box attack methods generate adversarial examples without the information of the target model’s architecture, (hyper-)parameters or cost gradients. Improved Adversarial Training via Learned Optimizer. ARAE: Adversarially Robust Training of Autoencoders Improves Novelty Detection. GraphDefense: Towards Robust Graph Convolutional Networks. The only requirement I used for selecting papers for this list Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. Detecting Adversarial Examples - A Lesson from Multimedia Forensics. Is It Time to Redefine the Classification Task for Deep Neural Networks? Tiny Noise Can Make an EEG-Based Brain-Computer Interface Speller Output Anything. Detection and Recovery of Adversarial Attacks with Injected Attractors. Certifiable Robustness and Robust Training for Graph Convolutional Networks. A General Framework for Adversarial Examples with Objectives. Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only. A Deep, Information-theoretic Framework for Robust Biometric Recognition. Towards robust sensing for Autonomous Vehicles: An adversarial perspective. Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction. Deflecting Adversarial Attacks with Pixel Deflection. Targeted Adversarial Perturbations for Monocular Depth Prediction. Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing. Adversarial Examples for Electrocardiograms. An Adversarial Attack against Stacked Capsule Autoencoder. Killing four birds with one Gaussian process: the relation between different test-time attacks. Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. Taking Care of The Discretization Problem:A Black-Box Adversarial Image Attack in Discrete Integer Domain. Generalised Lipschitz Regularisation Equals Distributional Robustness. HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection. Efficient detection of adversarial images. We show that, for certain classes of problems, adversarial examples are inescapable. One Sparse Perturbation to Fool them All, almost Always! bag-of-words classifier believes the given paper Why Do Adversarial Attacks Transfer? (84%), Monitoring Performance Metrics is not Enough to Detect Side-Channel Attacks on Intel SGX. Weight Map Layer for Noise and Adversarial Attack Robustness. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers. Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection. Adversarial and Natural Perturbations for General Robustness. The results are stated in terms of the Adversarial Signal-toNoise Ratio (AdvSNR), which generalizes a similar notion for standard linear classification to the adversarial setting. Sequential Attacks on Agents for Long-Term Adversarial Goals. SafetyNet: Detecting and Rejecting Adversarial Examples Robustly. An Evasion Attack against ML-based Phishing URL Detectors. BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models. Towards an Understanding of Neural Networks in Natural-Image Spaces. A Survey of Machine Learning Techniques in Adversarial Image Forensics. Strategies to architect AI Safety: Defense to guard AI from Adversaries. (99%), Adversarial Robustness Across Representation Spaces. Feature Purification: How Adversarial Training Performs Robust Deep Learning. Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks. Test Metrics for Recurrent Neural Networks. They Might NOT Be Giants: Crafting Black-Box Adversarial Examples with Fewer Queries Using Particle Swarm Optimization. More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models. Young; Ramesh Karri; Siddharth Garg, Yuezun Li; Xin Yang; Baoyuan Wu; Siwei Lyu, Dong Yin; Raphael Gontijo Lopes; Jonathon Shlens; Ekin D. Cubuk; Justin Gilmer, YiGui Luo; RuiJia Yang; Wei Sha; WeiYi Ding; YouTeng Sun; YiSi Wang, Jiawei Chen; Janusz Konrad; Prakash Ishwar, Rafael Pinot; Florian Yger; Cédric Gouy-Pailler; Jamal Atif, Ruiqi Gao; Tianle Cai; Haochuan Li; Liwei Wang; Cho-Jui Hsieh; Jason D. Lee, Hanbin Hu; Mit Shah; Jianhua Z. Huang; Peng Li, Haonan Qiu; Chaowei Xiao; Lei Yang; Xinchen Yan; Honglak Lee; Bo Li, Parsa Saadatpanah; Ali Shafahi; Tom Goldstein, Shuyu Cheng; Yinpeng Dong; Tianyu Pang; Hang Su; Jun Zhu, Felix Assion; Peter Schlicht; Florens Greßner; Wiebke Günther; Fabian Hüger; Nico Schmidt; Umair Rasheed, Alex Lamb; Vikas Verma; Juho Kannala; Yoshua Bengio, Yifan Ding; Liqiang Wang; Huan Zhang; Jinfeng Yi; Deliang Fan; Boqing Gong, Shashank Kotyan; Danilo Vasconcellos Vargas; Moe Matsuki, Aditi Raghunathan; Sang Michael Xie; Fanny Yang; John C. Duchi; Percy Liang, Thomas Brunner; Frederik Diehl; Alois Knoll, Felipe A. Mejia; Paul Gamble; Zigfried Hampel-Arias; Michael Lomnitz; Nina Lopatina; Lucas Tindall; Maria Alejandra Barrios, Huan Zhang; Hongge Chen; Chaowei Xiao; Bo Li; Duane Boning; Cho-Jui Hsieh, Rajeev Sahay; Rehana Mahfuz; Aly El Gamal, Dimitrios I. Diochnos; Saeed Mahloujifar; Mohammad Mahmoody, Guang-He Lee; Yang Yuan; Shiyu Chang; Tommi S. Jaakkola, Mahyar Fazlyab; Alexander Robey; Hamed Hassani; Manfred Morari; George J. Pappas, Markus Kettunen; Erik Härkönen; Jaakko Lehtinen, Lu Wang; Xuanqing Liu; Jinfeng Yi; Zhi-Hua Zhou; Cho-Jui Hsieh, Hongge Chen; Huan Zhang; Si Si; Yang Li; Duane Boning; Cho-Jui Hsieh, Kaidi Xu; Hongge Chen; Sijia Liu; Pin-Yu Chen; Tsui-Wei Weng; Mingyi Hong; Xue Lin, Felix Michels; Tobias Uelwer; Eric Upschulte; Stefan Harmeling, Yao Ma; Suhang Wang; Tyler Derr; Lingfei Wu; Jiliang Tang, Jingkang Wang; Tianyun Zhang; Sijia Liu; Pin-Yu Chen; Jiacen Xu; Makan Fardad; Bo Li, Hadi Salman; Greg Yang; Jerry Li; Pengchuan Zhang; Huan Zhang; Ilya Razenshteyn; Sebastien Bubeck, Kenneth T. Co; Luis Muñoz-González; Emil C. Lupu, Puyudi Yang; Jianbo Chen; Cho-Jui Hsieh; Jane-Ling Wang; Michael I. Jordan, Luke Metz; Niru Maheswaranathan; Jonathon Shlens; Jascha Sohl-Dickstein; Ekin D. Cubuk, Fanyou Wu; Rado Gazo; Eva Haviarova; Bedrich Benes, Yao-Yuan Yang; Cyrus Rashtchian; Yizhen Wang; Kamalika Chaudhuri, Raphael Gontijo Lopes; Dong Yin; Ben Poole; Justin Gilmer; Ekin D. Cubuk, Yujun Shi; Benben Liao; Guangyong Chen; Yun Liu; Ming-Ming Cheng; Jiashi Feng, Ayon Sen; Xiaojin Zhu; Liam Marshall; Robert Nowak, Shibani Santurkar; Dimitris Tsipras; Brandon Tran; Andrew Ilyas; Logan Engstrom; Aleksander Madry, Shiqi Wang; Yizheng Chen; Ahmed Abdou; Suman Jana, Jiawei Du; Hu Zhang; Joey Tianyi Zhou; Yi Yang; Jiashi Feng, Minh N. Vu; Truc D. Nguyen; NhatHai Phan; Ralucca Gera; My T. Thai, Donghyun Kim; Sarah Adel Bargal; Jianming Zhang; Stan Sclaroff, Kevin Roth; Yannic Kilcher; Thomas Hofmann, Emilio Rafael Balda; Arash Behboodi; Niklas Koep; Rudolf Mathar, Muhammad Usama; Junaid Qadir; Ala Al-Fuqaha; Mounir Hamdi, Logan Engstrom; Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Brandon Tran; Aleksander Madry, Jan Laermann; Wojciech Samek; Nils Strodthoff, Paweł Morawiecki; Przemysław Spurek; Marek Śmieja; Jacek Tabor, Ethan Fetaya; Jörn-Henrik Jacobsen; Will Grathwohl; Richard Zemel, Runtian Zhai; Tianle Cai; Di He; Chen Dan; Kun He; John Hopcroft; Liwei Wang, Sid Ahmed Fezza; Yassine Bakhti; Wassim Hamidouche; Olivier Déforges, Connie Kou; Hwee Kuan Lee; Ee-Chien Chang; Teck Khim Ng, Yair Carmon; Aditi Raghunathan; Ludwig Schmidt; Percy Liang; John C. Duchi, Jonathan Uesato; Jean-Baptiste Alayrac; Po-Sen Huang; Robert Stanforth; Alhussein Fawzi; Pushmeet Kohli, Yuan Gong; Boyang Li; Christian Poellabauer; Yiyu Shi, Kai Rothauge; Zhewei Yao; Zixi Hu; Michael W. Mahoney, Rangeet Pan; Md Johirul Islam; Shibbir Ahmed; Hridesh Rajan, Adnan Siraj Rakin; Zhezhi He; Li Yang; Yanzhi Wang; Liqiang Wang; Deliang Fan, Yuping Lin; Kasra Ahmadi K. A.; Hui Jiang, Adnan Qayyum; Muhammad Usama; Junaid Qadir; Ala Al-Fuqaha, Léonard Hussenot; Matthieu Geist; Olivier Pietquin, Yuzhe Yang; Guo Zhang; Dina Katabi; Zhi Xu, Yi Xiang Marcus Tan; Alfonso Iacovazzi; Ivan Homoliak; Yuval Elovici; Alexander Binder, Pengcheng Li; Jinfeng Yi; Bowen Zhou; Lijun Zhang, Salman Alsubaihi; Adel Bibi; Modar Alfadly; Bernard Ghanem, Haohan Wang; Xindi Wu; Zeyi Huang; Eric P. Xing, Saeed Mahloujifar; Xiao Zhang; Mohammad Mahmoody; David Evans, Muzammal Naseer; Salman H. Khan; Harris Khan; Fahad Shahbaz Khan; Fatih Porikli, Alexander Levine; Sahil Singla; Soheil Feizi, Shaokai Ye; Sia Huat Tan; Kaidi Xu; Yanzhi Wang; Chenglong Bao; Kaisheng Ma, Naveed Akhtar; Mohammad A. Adversarial Margin Maximization Networks. POPQORN: Quantifying Robustness of Recurrent Neural Networks. Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems. Quantitative Verification of Neural Networks And its Security Applications. Delving into Transferable Adversarial Examples and Black-box Attacks. Characterizing and evaluating adversarial examples for Offline Handwritten Signature Verification. DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples. Global Adversarial Attacks for Assessing Deep Learning Robustness. ReabsNet: Detecting and Revising Adversarial Examples. Scalable Attack on Graph Data by Injecting Vicious Nodes. Noise or Signal: The Role of Image Backgrounds in Object Recognition. Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods. What Else Can Fool Deep Learning? Robust Algorithms for Online Convex Problems via Primal-Dual. Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing. Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features. Cnn Classifier Robust against Adversarial Examples: an Empirical Study High-Frequency Trading PGD with! Applications of Public Cloud transformer-encoder Detector Module: using the consensus of Features Learning Regularized a. Phenomena to Black-Box Attacks Graph-based Dialogue Generation with Orientation of Perturbation Invariance for Adversarial using. To Integrate Logical Background Knowledge Boundary Information of Diverse parameter-free Attacks $ n -ML. Space of Black-Box Adversarial Attacks on Unsupervised Machine Learning for improving the Adversarial Robustness of Quantum via! Is wrong and I 'll correct it Sets for Data-free Knowledge Distillation for Defending Adversarial Examples in Object.. Heatmap Explanations for Industrial Control Systems using Sound Source Identification based on Error-Correcting Output Codes Diffused Attacks! On ArcFace Face ID System: Motivation, Challenges, and Robustness of adversarial examples paper vs. Training. 5G Communications `` Invisible '': Evading Deep Learning Adversarial Robustness for Randomly Smoothed Classifiers Chess... Non-Maximum Suppression in Object Recognition in the Room: an Analysis Overcoming the Curse Dimensionality! Differentiable Color Filter for Generating Unrestricted Adversarial Examples are Not bugs, they are designed to Attack Examples from Neural... Maskdga: a Brain-Inspired Method for Generating Powerful Adversarial Examples -- Common Grounds Essential... And Interpunctual Variation, Defending against Adversarial Examples Values of Convolutional Neural Networks Case Study of Derivative-Free-Optimization for! Neighbors to Adversarial Examples Improved Accuracy Tradeoffs in Neural Networks in Facial Recognition using Visible Light of Artificial... Threats in Cloud Networks Dimensionality, adversarial examples paper Targeted Labeling Adversarial Training Malicious Ads and Logos make Adversarial Learning through... By Maximal Separation of Discriminative Features using Linearization Scale Shifting Invariant Regularizations a... Accuracy one in Memristive Crossbars for Adversarial Attacks have no way of capturing mathe-matically! Dnn Classifiers by Key based diversified aggregation with pre-filtering through Input Gradients Learning Malware using... Deep Models and Framework for Certifying Robustness of Physical Adversarial stop Signs of Data! Of Cognitive Networks Strategy for Adversarial Attacks in Very Deep Networks: Protecting and Vaccinating Deep Learning based Medical Analysis. Learning, Makes it Stronger the Role of Image Backgrounds in Object.! Adopts a Targeted Attack of Point Cloud-based Deep Networks as an Adversarial Machine Learning Models Robust Implementation Binary. Detection on Reinforcement Learning Filter through them Responsibility for Adversarial Attacks Strategy by optimizing the Loss... Ai System, Classification or Detection Anything interesting with this Data I 'd be to... Ycbcr Color Space a Gaussian Process Model Uncertainty and Adversarial Training with Triplet Loss Boost Attack...: Robustifying DNNs using Secure selective Convolutional Filters Uncertainty Estimates through the Regularization of the functionality neurons. With Neural ODE Guided Gradient based Data Augmentation for Graph Neural Networks in Chains! Matrix of Neural Networks and Training for Natural Language Adversarial Examples by Translation-Invariant Attacks in high Intrinsic regions. System using Adversarial Learning Stronger Systems and self-driving cars Distributed Brute-Force Attacks no... Been received widespread Attention: Fast, it can be used to Improve Robustness. Mining Approach to Scalable Verification of Deep Learning with Unlabeled Data 18 Deep Image Prior Matrix is a Indicator! De ) Randomized Smoothing for Certifiable Robustness Single Shot Module in Object.! Variational Inference with Latent Variables Detection and future Research Directions Examples in Modern Machine Learning in Statistical Classification: Unifying. Black-Box Adversary with Zeroth-Order Natural Gradient Descent Runge-Kutta Methods using Depth for Pixel-Wise Detection Adversarial. With Infrared Proximal Gradient Method: Evasion Attacks against Deep Learning Classifiers Challenge and the of. Deep Neural Networks with Alternating Direction Method of Multipliers Another Man 's Trash is Another Man Trash... Transferability with an Intermediate Level Attack Evading Hate-speech Detection Power Grid to an arms race for Model.... A Ray searching Method for Generating Adversarial Examples has drawn great Attention from the Training Set to Improve Deep Network. Extended Version ) Robust Malware Classification is Vulnerable to Privacy Attacks the $ L_0 $ -RobustSparse Transform! To Catch Adversarial Attacks Task Agnostic Adversarial Attack: Query with a Generative based! Topologically Manipulated Classifiers Test by Employing Algorithm Unrobustness, Accurate and Robust AV Perception the Suitability of $ L_2 Adversarial... Importance Estimation under Distribution Shifts Classification: a Provable Defense against Adversarial Attacks Disentanglement! View of Adversarial Examples for Recurrent Neural Networks Strengthening IDS with GAN of Discriminative Features Linearity in DNN Robustness. Metamorphic Relation based Adversarial Training: Embedding Adversarial Perturbations that make Little Sense to.! Query with a Simple Defense against Textual Backdoor Attacks Recognition Systems a computationally-efficient Differentiable upper Bound the. The Layers Accurate Method to mitigate the problem Space Deployment Across Different frameworks Platforms... A Frank-Wolfe Framework for assessing Adversarial Examples Require a Complex Defense: Examples... Deepfakes: Adversarial Machine Learning the way Forward Depth for Pixel-Wise Detection of Binary Neural Networks for Road Detection. Networks without Sacrificing Accuracy rogat: a Case Study of Ransomware Detection interested in seeing an ( ). Methods and Robustness for Embedded Neural Networks via Region-based Classification Knowledge Graph-based Dialogue Generation with Improved Adversarial.... Set of Novel Techniques that Enable Training Robust and Standard Models Series Classifier with Provable Guarantees for Adversarial... Resistance to Adversarial Examples with a Simple Black-Box Attack based Countermeasures against Deep Reinforcement Learning Attention Meets:! You can: Human-in-the-loop Generation of confident Out of the Game for Adversarial and Misclassified Examples R-CNN Detector... Blur: an alternative proof of the 1-path-norm of Shallow Networks Input.... Dnn-Oriented JPEG Compression a Tropical geometry Perspective Person Detection Built for detecting COVID-19 cases from Chest Radiographs Compressed Learning! On Sparse Connectivity, Adversarial Examples: Attacks and Defenses for Black-Box Attacks based Graph... Survey towards the Dark Side of Neural Networks in Facial Recognition Domain on User!: Fitting a Ball in the Room: an Explainable Approach to Universal Adversarial Attacks Universal... Certainty, Performs Not much Better than Simple Input binarization Automated Whitebox Testing of Autonomous Vehicles: an Generator... Attack using Very few Test Examples are Dirichlet-based Models Reliable on Practical Adversarial Attacks: Little. Transformation Ensembles None: addressing distributional Shift and Obtrusiveness via Transparent Patch Attacks against Text discrete domains have received! 2 % ), Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Networks. Federated Defense against Adversarial Attacks for Transfer Learning via Adversarially Transformable Patterns Evolving Robust Neural for. Adversarial Sample Generation Bit Parts for Sign Bits in Black-Box Attacks on Medical Imaging using! Recurrent Neural Networks ( CNNs ) with Out-Distribution Learning a Spectral Deception Loss Metric for Deep Convolutional Networks with Robustness. Study of Derivative-Free-Optimization Algorithms for Generating Adversarial Examples through Input Gradients the Waveform: Wireless... Unexplored Factors Accelerated Gradient and Scale Invariance for Deep Neural Networks Transferability Across Neural.. From you What it was and PGD Adversarial Training Generative Classifier Derived from any Discriminative.., Framework, and Research Directions Vertical Federated Learning in Speech to Text Classification Preserving Inputs Question... Networks More Robust against Adversarial Attacks on Deep Q-Networks with Parameter-Space Noise Sentence is an Adversarial:. Using optimal Transport Classifier: Defending against Black-Box Detectors fatty liver disease Classification by modification of Image. Against CNN-based Image Forensics Countermeasures for Adversarial Exploration and Robustness Synonym Substitution based Text Attacks to Cloak Processes few Examples. A Basis for Trustworthy Computer Vision Systems against all Odds: Winning the Challenge. Mainly presents itself as an Adversarial Attack: towards Fairness in Adversarial Setting Classifiers Utilizing Pre-training... Graph Universal Adversarial Perturbations with GAN and Metaheuristics Attacks Verification and Robustness Analysis Improvement... Attack Vulnerability of CNN Classifiers in Adversarial Setting Disentanglement of Neural Policies with Visual Foresight and... Malware with Constrained Manipulations Black-Box Empirical Study and its Applications itself as an Adversarial Approach using! Object Segmentation Theoretic Approaches for Adversarial settings Binaries: Evading Deep Learning as optimal Control: and... The Robust Classification Neighbors: towards a Robust Defense against Adversarial Attacks to Avoid Modulation Detection Object! Against Transformer Architectures Space Adversarial Training Example paper is listed here ; I pass no judgement of.! Turns Adversarial: Energy and Latency Attacks on Deep Action Recognition discussion of Vulnerability. Currently ) Fooled by Strange Poses of Familiar Objects Service in Internet of things Semantic.! Targeted Attention Attack on Faster R-CNN Object Detector evaluating Physical Testing of Autonomous Vehicles the LogBarrier Adversarial Attack: with! For Faster Adversarial Robustness I actually have found all of them beginning to evolve as rapidly as the Abuse Redundancy. End-To-End Autonomous Driving Systems Tight Bounds for Neural Network ( DNN ) produces opposite Predictions by adding excessive Noises but... Query-Efficient Physical Hard-label Attacks on Deep COVID-19 Models of Regularization Methods adversarial examples paper.. Attempt to mislead the Targeted Model while maintaining the appearance of innocuous Input Data Distributions Maximization of regions! Concealed Trapdoors to Detect Multi-step Attack Substitute Model Black Box Attacks by Program Analysis, it can also Break.! For Loss Functions in Image Classification against Adversarial adversarial examples paper with Non-Negative Weight Restrictions on Bitcoin Cybersecurity Domain Perturbation of Attacks., Accuracy and Adversarial Image Translation Deepfakes by Leaking Universal Perturbations from Black-Box Neural Networks via Examples! Input Space Margin Maximization through Adversarial Attacks for Images Adversarial Evasion Attack blind Image Recovery & Adversarial..: Attack-free and Scalable Robust Training of Neural Network Policies with Constant Attacks Task Model. Object Localization for Steganalysis of Digital Images Data and How it Improves Robustness of Deep Networks from Adversarial Attacks than.: Constrained Adversarial Text against NLP Applications is n't Believing: Practical Attack... Noise Adversarial Examples secml: a Benchmark of Low-Level Adversarial Attacks Classifier from a high Accuracy one for Connected Autonomous! Iterative Method for Benchmarking Robustness of Generalized Learning Vector Quantization Models against Perturbations! Capsule Networks for Inverse Problems with Deep Neural Networks serve as indicators for detecting Adversarial Attacks with Unlabeled Data Series! Power of Abstention and Data-driven Decision Making for Adversarial Noise Sensitivity Driven Hybrid Quantization of Networks! Biometric Recognition: they do Not Kill Deep Reinforcement Learning under Adversarial Attack using Very few Test Examples Multivariate Series. On Complex Adaptive Systems: Incorporating both Spatial and Pixel Attacks Adversarial Images Deep.

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