FineFool: Fine Object Contour Attack via Attention. Recovery Guarantees for Compressible Signals with Adversarial Noise. Improving Adversarial Robustness of Ensembles with Diversity Training. How Does Mixup Help With Robustness and Generalization? Adversarial VC-dimension and Sample Complexity of Neural Networks. RAID: Randomized Adversarial-Input Detection for Neural Networks. Adversarial Examples for Electrocardiograms. Theoretical evidence for adversarial robustness through randomization. Towards Robust Fine-grained Recognition by Maximal Separation of Discriminative Features. Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks. Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks. Bias-based Universal Adversarial Patch Attack for Automatic Check-out. Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks. A Surprising Density of Illusionable Natural Speech. Adversarial Diversity and Hard Positive Generation. An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods. Summit: Scaling Deep Learning Interpretability by Visualizing Activation and Attribution Summarizations. Perceptual Evaluation of Adversarial Attacks for CNN-based Image Classification. Against All Odds: Winning the Defense Challenge in an Evasion Competition with Diversification. Maximum Mean Discrepancy is Aware of Adversarial Attacks. SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations. HeNet: A Deep Learning Approach on Intel$^\circledR$ Processor Trace for Effective Exploit Detection. ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples. Towards an Intrinsic Definition of Robustness for a Classifier. ®å¼‚), We develop an interpretability-aware defensive scheme built only on promoting robust interpretation (without the need for resorting to adversarial loss minimization. Weakly Supervised Localization using Min-Max Entropy: an Interpretable Framework. DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples. Attack Agnostic Statistical Method for Adversarial Detection. (1%), Practical No-box Adversarial Attacks against DNNs. Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization. Enabling Fast and Universal Audio Adversarial Attack Using Generative Model. Easy to Fool? Adversarial Attacks against Deep Saliency Models. In this paper, we explore the landscape around adversarial training in a … Adversarial Rain Attack on DNN Perception. Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy. Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection. Verification of Recurrent Neural Networks Through Rule Extraction. MetaSimulator: Simulating Unknown Target Models for Query-Efficient Black-box Attacks. (99%), Improving the Transferability of Adversarial Examples with the Adam Optimizer. Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking. Analyzing Accuracy Loss in Randomized Smoothing Defenses. (99%), Just One Moment: Inconspicuous One Frame Attack on Deep Action Recognition. On Adversarial Examples for Character-Level Neural Machine Translation. Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification SOCRATES: Towards a Unified Platform for Neural Network Verification. On Robustness of Neural Ordinary Differential Equations. Vulnerability Under Adversarial Machine Learning: Bias or Variance? Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise. An Adversarial Approach for Explaining the Predictions of Deep Neural Networks. A Study of the Transformation-based Ensemble Defence. Understanding Misclassifications by Attributes. Neural Networks in Adversarial Setting and Ill-Conditioned Weight Space. ADef: an Iterative Algorithm to Construct Adversarial Deformations. Gradient-based Analysis of NLP Models is Manipulable. Dissecting Deep Networks into an Ensemble of Generative Classifiers for Robust Predictions. Understanding Catastrophic Overfitting in Single-step Adversarial Training. Randomized Prediction Games for Adversarial Machine Learning. EagleEye: Attack-Agnostic Defense against Adversarial Inputs (Technical Report). judgement calls as to whether or not any given paper is Automatic Generation of Adversarial Examples for Interpreting Malware Classifiers. Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models. Measuring the Transferability of Adversarial Examples. TensorShield: Tensor-based Defense Against Adversarial Attacks on Images. The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection. Verification of Binarized Neural Networks via Inter-Neuron Factoring. Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers. Black-box Smoothing: A Provable Defense for Pretrained Classifiers. OpenAttack: An Open-source Textual Adversarial Attack Toolkit. Defensive Approximation: Enhancing CNNs Security through Approximate Computing. Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples. Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model. Representation of White- and Black-Box Adversarial Examples in Deep Neural Networks and Humans: A Functional Magnetic Resonance Imaging Study. Evaluating Neural Machine Comprehension Model Robustness to Noisy Inputs and Adversarial Attacks. (99%), A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations. Adversarial Attacks on Grid Events Classification: An Adversarial Machine Learning Approach. Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. Total Deep Variation: A Stable Regularizer for Inverse Problems. Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors. Adversarial Robustness: Softmax versus Openmax. Transferable Adversarial Robustness using Adversarially Trained Autoencoders. (26%), Privacy Leakage of Real-World Vertical Federated Learning. Black-box Backdoor Attack on Face Recognition Systems. Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks. Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks. We tackle this problem by showing that, under mild conditions on the dataset distribution, any deterministic classifier can be outperformed by a randomized one. The Vulnerabilities of Graph Convolutional Networks: Stronger Attacks and Defensive Techniques. Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model. Influence of discriminative features on deep network boundaries. Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers. A Black-box Adversarial Attack for Poisoning Clustering. Tactics of Adversarial Attack on Deep Reinforcement Learning Agents. Universal, transferable and targeted adversarial attacks. Is Deep Learning Safe for Robot Vision? ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems. Hold me tight! With Friends Like These, Who Needs Adversaries? Are Odds Really Odd? Towards Sharper First-Order Adversary with Quantized Gradients. AdvFoolGen: Creating Persistent Troubles for Deep Classifiers. Adversarial camera stickers: A physical camera-based attack on deep learning systems. Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ? Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis. Advbox: a toolbox to generate adversarial examples that fool neural networks. Below is the list of papers I recommend reading to become familiar with the specific sub-field of evasion attacks on machine learning systems (i.e., adversarial examples). Robustness to Adversarial Perturbations in Learning from Incomplete Data. Vulnerabilities of Connectionist AI Applications: Evaluation and Defence. Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks. Adversary Detection in Neural Networks via Persistent Homology. 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. Sponge Examples: Energy-Latency Attacks on Neural Networks. Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics. On the Limitation of MagNet Defense against $L_1$-based Adversarial Examples. Feature-level Malware Obfuscation in Deep Learning. ROSA: Robust Salient Object Detection against Adversarial Attacks. Evading Classifiers by Morphing in the Dark. weight pruning is essential for reducing the model size in the adversarial setting. HopSkipJumpAttack: A Query-Efficient Decision-Based Attack. Defective Convolutional Layers Learn Robust CNNs. Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks. Robust Neural Machine Translation: Modeling Orthographic and Interpunctual Variation. QEBA: Query-Efficient Boundary-Based Blackbox Attack. Towards Noise-Robust Neural Networks via Progressive Adversarial Training. Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack. Security and Machine Learning in the Real World. Explainable Black-Box Attacks Against Model-based Authentication. Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment. they are easy to defend against via simple statistical methods. Explaining Transferability of Evasion and Poisoning Attacks. A New Defense Against Adversarial Images: Turning a Weakness into a Strength. Design and Interpretation of Universal Adversarial Patches in Face Detection. Adaptive Generation of Unrestricted Adversarial Inputs. Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks. (31%), De-STT: De-entaglement of unwanted Nuisances and Biases in Speech to Text System using Adversarial Forgetting. Understanding and Improving Fast Adversarial Training. A Direct Approach to Robust Deep Learning Using Adversarial Networks. Here we present an opposite perspective: ad- Adversarial Reprogramming of Neural Networks. 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. Enhanced Attacks on Defensively Distilled Deep Neural Networks. Exploiting the Inherent Limitation of L0 Adversarial Examples. Universal adversarial examples in speech command classification. Physical Adversarial Attack on Vehicle Detector in the Carla Simulator. Optimization-Guided Binary Diversification to Mislead Neural Networks for Malware Detection. We introduce a transformed deadzone layer into the defense network, which consists of an orthonormal transform and a deadzone-based activation function, to destroy the sophisticated noise pattern of adversarial attacks. Towards Visual Distortion in Black-Box Attacks. SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. Fooling Network Interpretation in Image Classification. Analysis of classifiers' robustness to adversarial perturbations. Persistency of Excitation for Robustness of Neural Networks. PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards. Improving Machine Reading Comprehension via Adversarial Training. A geometry-inspired decision-based attack. Learning Adversary-Resistant Deep Neural Networks. Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks. The Human Visual System and Adversarial AI. Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance. Background Adversarial Attack — Black-box and White-box. Can Adversarially Robust Learning Leverage Computational Hardness? mixup: Beyond Empirical Risk Minimization. How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples. SoK: Certified Robustness for Deep Neural Networks. Statistical Guarantees for the Robustness of Bayesian Neural Networks. Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks. Query-Efficient Physical Hard-Label Attacks on Deep Learning Visual Classification. Towards Certified Robustness of Metric Learning. Integer Programming-based Error-Correcting Output Code Design for Robust Classification. Efficient Robust Training via Backward Smoothing. Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning. (99%), Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks. Attack on Multi-Node Attention for Object Detection. 1Throughout the paper… Revisiting Batch Normalization for Improving Corruption Robustness. Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs. Understanding and Quantifying Adversarial Examples Existence in Linear Classification. The gap between theory and practice in function approximation with deep neural networks. On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models. Black, Saeid Samizade; Zheng-Hua Tan; Chao Shen; Xiaohong Guan, Karl M. Koerich; Mohammad Esmailpour; Sajjad Abdoli; Alceu S. Jr. Britto; Alessandro L. Koerich, Dan Peng; Zizhan Zheng; Linhao Luo; Xiaofeng Zhang, Jan Philip Göpfert; Heiko Wersing; Barbara Hammer, Yangjun Ruan; Yuanhao Xiong; Sashank Reddi; Sanjiv Kumar; Cho-Jui Hsieh, Sven Gowal; Jonathan Uesato; Chongli Qin; Po-Sen Huang; Timothy Mann; Pushmeet Kohli, Yujia Qin; Fanchao Qi; Sicong Ouyang; Zhiyuan Liu; Cheng Yang; Yasheng Wang; Qun Liu; Maosong Sun, Songxiang Liu; Haibin Wu; Hung-yi Lee; Helen Meng, Mahdieh Abbasi; Changjian Shui; Arezoo Rajabi; Christian Gagne; Rakesh Bobba, Simran Kaur; Jeremy Cohen; Zachary C. Lipton, Qing Guo; Xiaofei Xie; Lei Ma; Zhongguo Li; Wei Feng; Yang Liu, Yasaman Esfandiari; Aditya Balu; Keivan Ebrahimi; Umesh Vaidya; Nicola Elia; Soumik Sarkar, Yogesh Balaji; Tom Goldstein; Judy Hoffman, Anindya Sarkar; Nikhil Kumar Gupta; Raghu Iyengar, Kaidi Xu; Gaoyuan Zhang; Sijia Liu; Quanfu Fan; Mengshu Sun; Hongge Chen; Pin-Yu Chen; Yanzhi Wang; Xue Lin, Tao Yu; Shengyuan Hu; Chuan Guo; Wei-Lun Chao; Kilian Q. Weinberger, Anindya Sarkar; Anirudh Sunder Raj; Raghu Sesha Iyengar, Sadaf Gulshad; Zeynep Akata; Jan Hendrik Metzen; Arnold Smeulders, Mikhail Pautov; Grigorii Melnikov; Edgar Kaziakhmedov; Klim Kireev; Aleksandr Petiushko, Fuyuan Zhang; Sankalan Pal Chowdhury; Maria Christakis, David Stutz; Matthias Hein; Bernt Schiele, Xiangyi Chen; Sijia Liu; Kaidi Xu; Xingguo Li; Xue Lin; Mingyi Hong; David Cox, Derek Derui; Wang; Chaoran Li; Sheng Wen; Surya Nepal; Yang Xiang, Edgar Kaziakhmedov; Klim Kireev; Grigorii Melnikov; Mikhail Pautov; Aleksandr Petiushko, Hanshu Yan; Jiawei Du; Vincent Y. F. Tan; Jiashi Feng, Hadi Abdullah; Muhammad Sajidur Rahman; Washington Garcia; Logan Blue; Kevin Warren; Anurag Swarnim Yadav; Tom Shrimpton; Patrick Traynor, Marco Schreyer; Timur Sattarov; Bernd Reimer; Damian Borth, Matteo Terzi; Gian Antonio Susto; Pratik Chaudhari, Ali Dabouei; Sobhan Soleymani; Fariborz Taherkhani; Jeremy Dawson; Nasser M. Nasrabadi, Guangyu Shen; Chengzhi Mao; Junfeng Yang; Baishakhi Ray, Laurent Meunier; Jamal Atif; Olivier Teytaud, Gavin S. Hartnett; Andrew J. Lohn; Alexander P. Sedlack, Kaleel Mahmood; Phuong Ha Nguyen; Lam M. Nguyen; Thanh Nguyen; Dijk Marten van, Adith Boloor; Karthik Garimella; Xin He; Christopher Gill; Yevgeniy Vorobeychik; Xuan Zhang, Xuan Li; Yuchen Lu; Peng Xu; Jizong Peng; Christian Desrosiers; Xue Liu, Lakshya Jain; Wilson Wu; Steven Chen; Uyeong Jang; Varun Chandrasekaran; Sanjit Seshia; Somesh Jha, Bowen Zhang; Benedetta Tondi; Mauro Barni, Yang Zhang; Shiyu Chang; Mo Yu; Kaizhi Qian, Wenqi Wei; Ling Liu; Margaret Loper; Ka-Ho Chow; Emre Gursoy; Stacey Truex; Yanzhao Wu, Angelo Sotgiu; Ambra Demontis; Marco Melis; Battista Biggio; Giorgio Fumera; Xiaoyi Feng; Fabio Roli, Sijia Liu; Songtao Lu; Xiangyi Chen; Yao Feng; Kaidi Xu; Abdullah Al-Dujaili; Minyi Hong; Una-May O'Reilly, Aniruddha Saha; Akshayvarun Subramanya; Koninika Patil; Hamed Pirsiavash, Indu Ilanchezian; Praneeth Vepakomma; Abhishek Singh; Otkrist Gupta; G. N. Srinivasa Prasanna; Ramesh Raskar, Hong Liu; Mingsheng Long; Jianmin Wang; Michael I. Jordan, Muhammad Usama; Muhammad Asim; Junaid Qadir; Ala Al-Fuqaha; Muhammad Ali Imran, Salah-ud-din Farooq; Muhammad Usama; Junaid Qadir; Muhammad Ali Imran, Arjun Nitin Bhagoji; Daniel Cullina; Prateek Mittal, Nilesh A. Ahuja; Ibrahima Ndiour; Trushant Kalyanpur; Omesh Tickoo, Chen Zhu; Yu Cheng; Zhe Gan; Siqi Sun; Tom Goldstein; Jingjing Liu, Brandon Laughlin; Christopher Collins; Karthik Sankaranarayanan; Khalil El-Khatib, Liang Chen; Paul Bentley; Kensaku Mori; Kazunari Misawa; Michitaka Fujiwara; Daniel Rueckert, Minhao Cheng; Simranjit Singh; Patrick Chen; Pin-Yu Chen; Sijia Liu; Cho-Jui Hsieh, Jinyuan Jia; Ahmed Salem; Michael Backes; Yang Zhang; Neil Zhenqiang Gong, Chuanbiao Song; Kun He; Jiadong Lin; Liwei Wang; John E. Hopcroft, Guoliang Dong; Jingyi Wang; Jun Sun; Yang Zhang; Xinyu Wang; Ting Dai; Jin Song Dong; Xingen Wang, Aminollah Khormali; Ahmed Abusnaina; Songqing Chen; DaeHun Nyang; Aziz Mohaisen, Tong Wu; Liang Tong; Yevgeniy Vorobeychik, Jihyeun Yoon; Kyungyul Kim; Jongseong Jang, Michael Thomas Smith; Kathrin Grosse; Michael Backes; Mauricio A Alvarez, Sekitoshi Kanai; Yasutoshi Ida; Yasuhiro Fujiwara; Masanori Yamada; Shuichi Adachi, Aishan Liu; Xianglong Liu; Chongzhi Zhang; Hang Yu; Qiang Liu; Junfeng He, Basemah Alshemali; Alta Graham; Jugal Kalita, Han Xu; Yao Ma; Haochen Liu; Debayan Deb; Hui Liu; Jiliang Tang; Anil Jain, Rayan Mosli; Matthew Wright; Bo Yuan; Yin Pan, Wanting Yu; Hongyi Yu; Lingyun Jiang; Mengli Zhang; Kai Qiao; Linyuan Wang; Bin Yan, Paul Temple; Mathieu Acher; Gilles Perrouin; Battista Biggio; Jean-marc Jezequel; Fabio Roli, Chongzhi Zhang; Aishan Liu; Xianglong Liu; Yitao Xu; Hang Yu; Yuqing Ma; Tianlin Li, Qianyu Guo; Sen Chen; Xiaofei Xie; Lei Ma; Qiang Hu; Hongtao Liu; Yang Liu; Jianjun Zhao; Xiaohong Li, Gilad Cohen; Guillermo Sapiro; Raja Giryes, Haochen Liu; Tyler Derr; Zitao Liu; Jiliang Tang, Zudi Lin; Hanspeter Pfister; Ziming Zhang, Chaomin Shen; Yaxin Peng; Guixu Zhang; Jinsong Fan, Yannik Potdevin; Dirk Nowotka; Vijay Ganesh, Pratik Vaishnavi; Kevin Eykholt; Atul Prakash; Amir Rahmati, Eitan Rothberg; Tingting Chen; Luo Jie; Hao Ji, Hang Yu; Aishan Liu; Xianglong Liu; Jichen Yang; Chongzhi Zhang, Lifeng Huang; Chengying Gao; Yuyin Zhou; Changqing Zou; Cihang Xie; Alan Yuille; Ning Liu, Aditya Ganeshan; B. S. Vivek; R. Venkatesh Babu, Zhenxin Xiao; Kai-Wei Chang; Cho-Jui Hsieh, Pratyush Maini; Eric Wong; J. Zico Kolter, Xugang Wu; Xiaoping Wang; Xu Zhou; Songlei Jian, Yichao Zhou; Jyun-Yu Jiang; Kai-Wei Chang; Wei Wang, Yu-Lun Hsieh; Minhao Cheng; Da-Cheng Juan; Wei Wei; Wen-Lian Hsu; Cho-Jui Hsieh, Yiren Zhao; Ilia Shumailov; Han Cui; Xitong Gao; Robert Mullins; Ross Anderson, Xian Yeow Lee; Sambit Ghadai; Kai Liang Tan; Chinmay Hegde; Soumik Sarkar, Zhouxing Shi; Minlie Huang; Ting Yao; Jingfang Xu, Robin Jia; Aditi Raghunathan; Kerem Göksel; Percy Liang, Po-Sen Huang; Robert Stanforth; Johannes Welbl; Chris Dyer; Dani Yogatama; Sven Gowal; Krishnamurthy Dvijotham; Pushmeet Kohli, Chengzhi Mao; Ziyuan Zhong; Junfeng Yang; Carl Vondrick; Baishakhi Ray, Quanyu Dai; Xiao Shen; Liang Zhang; Qiang Li; Dan Wang, Ling Liu; Wenqi Wei; Ka-Ho Chow; Margaret Loper; Emre Gursoy; Stacey Truex; Yanzhao Wu, Bang Wu; Xiangwen Yang; Shuo Wang; Xingliang Yuan; Cong Wang; Carsten Rudolph, Alessandro Cennamo; Ido Freeman; Anton Kummert, Chuanguang Yang; Zhulin An; Hui Zhu; Xiaolong Hu; Kun Zhang; Kaiqiang Xu; Chao Li; Yongjun Xu, Zhibo Wang; Siyan Zheng; Mengkai Song; Qian Wang; Alireza Rahimpour; Hairong Qi, Giorgos Tolias; Filip Radenovic; Ond{ř}ej Chum, Dou Goodman; Xingjian Li; Jun Huan; Tao Wei, Daniel Kang; Yi Sun; Dan Hendrycks; Tom Brown; Jacob Steinhardt, Marcus Soll; Tobias Hinz; Sven Magg; Stefan Wermter, Ka-Ho Chow; Wenqi Wei; Yanzhao Wu; Ling Liu, Xianfeng Tang; Yandong Li; Yiwei Sun; Huaxiu Yao; Prasenjit Mitra; Suhang Wang, Eric Wallace; Shi Feng; Nikhil Kandpal; Matt Gardner; Sameer Singh, Xiao Wang; Siyue Wang; Pin-Yu Chen; Yanzhi Wang; Brian Kulis; Xue Lin; Peter Chin, Fnu Suya; Jianfeng Chi; David Evans; Yuan Tian, Sahil Shah; Naman jain; Abhishek Sharma; Arjun Jain, Zhendong Zhang; Cheolkon Jung; Xiaolong Liang, Yuh-Shyang Wang; Tsui-Wei Weng; Luca Daniel, Jiadong Lin; Chuanbiao Song; Kun He; Liwei Wang; John E. Hopcroft, Jiangfan Han; Xiaoyi Dong; Ruimao Zhang; Dongdong Chen; Weiming Zhang; Nenghai Yu; Ping Luo; Xiaogang Wang, Debayan Deb; Jianbang Zhang; Anil K. Jain, Seungju Cho; Tae Joon Jun; Byungsoo Oh; Daeyoung Kim, Rahim Taheri; Reza Javidan; Mohammad Shojafar; Zahra Pooranian; Ali Miri; Mauro Conti, Sobhan Soleymani; Ali Dabouei; Jeremy Dawson; Nasser M. Nasrabadi, Sajjad Abdoli; Luiz G. Hafemann; Jerome Rony; Ismail Ben Ayed; Patrick Cardinal; Alessandro L. Koerich, Youcheng Sun; Hana Chockler; Xiaowei Huang; Daniel Kroening, Chen Ma; Chenxu Zhao; Hailin Shi; Li Chen; Junhai Yong; Dan Zeng, Wenjian Luo; Chenwang Wu; Nan Zhou; Li Ni, Aram-Alexandre Pooladian; Chris Finlay; Tim Hoheisel; Adam Oberman, Lea Schönherr; Thorsten Eisenhofer; Steffen Zeiler; Thorsten Holz; Dorothea Kolossa, Heng Chang; Yu Rong; Tingyang Xu; Wenbing Huang; Honglei Zhang; Peng Cui; Wenwu Zhu; Junzhou Huang, Akshay Chaturvedi; Abijith KP; Utpal Garain, Puneet Mangla; Surgan Jandial; Sakshi Varshney; Vineeth N Balasubramanian, Muhammad Usama; Junaid Qadir; Ala Al-Fuqaha, Zheng Liu; Jinnian Zhang; Varun Jog; Po-Ling Loh; Alan B McMillan, Christina Göpfert; Jan Philip Göpfert; Barbara Hammer, Utku Ozbulak; Messem Arnout Van; Neve Wesley De, Hossein Hosseini; Sreeram Kannan; Radha Poovendran, Di Jin; Zhijing Jin; Joey Tianyi Zhou; Peter Szolovits, Pu Zhao; Sijia Liu; Pin-Yu Chen; Nghia Hoang; Kaidi Xu; Bhavya Kailkhura; Xue Lin, Soufiane Belharbi; Jérôme Rony; Jose Dolz; Ismail Ben Ayed; Luke McCaffrey; Eric Granger, Xingjun Ma; Yuhao Niu; Lin Gu; Yisen Wang; Yitian Zhao; James Bailey; Feng Lu, Qian Huang; Isay Katsman; Horace He; Zeqi Gu; Serge Belongie; Ser-Nam Lim, Chaowei Xiao; Xinlei Pan; Warren He; Jian Peng; Mingjie Sun; Jinfeng Yi; Bo Li; Dawn Song, Alessandro Erba; Riccardo Taormina; Stefano Galelli; Marcello Pogliani; Michele Carminati; Stefano Zanero; Nils Ole Tippenhauer, Dan Hendrycks; Kevin Zhao; Steven Basart; Jacob Steinhardt; Dawn Song, Yulong Cao; Chaowei Xiao; Benjamin Cyr; Yimeng Zhou; Won Park; Sara Rampazzi; Qi Alfred Chen; Kevin Fu; Z. Morley Mao, Yuxin Ma; Tiankai Xie; Jundong Li; Ross Maciejewski, Guoping Zhao; Mingyu Zhang; Jiajun Liu; Ji-Rong Wen, Steven Chen; Nicholas Carlini; David Wagner, Yulong Cao; Chaowei Xiao; Dawei Yang; Jing Fang; Ruigang Yang; Mingyan Liu; Bo Li, Rohan Reddy Mekala; Gudjon Einar Magnusson; Adam Porter; Mikael Lindvall; Madeline Diep, Letao Liu; Martin Saerbeck; Justin Dauwels, Yao Qin; Nicholas Frosst; Sara Sabour; Colin Raffel; Garrison Cottrell; Geoffrey Hinton, Chongli Qin; James Martens; Sven Gowal; Dilip Krishnan; Krishnamurthy Dvijotham; Alhussein Fawzi; Soham De; Robert Stanforth; Pushmeet Kohli, Vinod Subramanian; Emmanouil Benetos; Ning Xu; SKoT McDonald; Mark Sandler, Thomas Gittings; Steve Schneider; John Collomosse, Stefano Calzavara; Claudio Lucchese; Gabriele Tolomei; Seyum Assefa Abebe; Salvatore Orlando, Nader Asadi; AmirMohammad Sarfi; Sahba Tahsini; Mahdi Eftekhari, Wieland Brendel; Jonas Rauber; Matthias Kümmerer; Ivan Ustyuzhaninov; Matthias Bethge, Nir Morgulis; Alexander Kreines; Shachar Mendelowitz; Yuval Weisglass, Dan Hendrycks; Mantas Mazeika; Saurav Kadavath; Dawn Song, Xian Yeow Lee; Aaron Havens; Girish Chowdhary; Soumik Sarkar, Shashank Kotyan; Danilo Vasconcellos Vargas, Yifeng Li; Lingxi Xie; Ya Zhang; Rui Zhang; Yanfeng Wang; Qi Tian, Teodora Baluta; Shiqi Shen; Shweta Shinde; Kuldeep S. Meel; Prateek Saxena, Kang Liu; Haoyu Yang; Yuzhe Ma; Benjamin Tan; Bei Yu; Evangeline F. Y. Learning with Local Errors Recognition in the Union of Polytopes: Multimedia Forensics vs Computer Vision Models a Human-perception Defense... Segmentation against Adversarial Attack on Deep Learning Systems with Adversarial Training Robust of... Detecting Misclassification Errors in Neural Networks Manifold smoothness and Adversarial Training on Different Types Neural... Cleaning with Feedback Loops for Defending Adversarial Attacks on Convolutional Neural Networks Robustness of Deep Learning optimal... Attriguard: a Unified Platform for Neural Network Robustness and Common Perturbation Robustness Independent attributes Data Poisoning Attacks API.... To Attack Domain Generation Algorithm Classifiers a Strong Baseline for Natural Language Understanding Conditional! Stickers: a Deeper Look at Scoring Dialogue Responses a Tropical geometry.. For Connected and Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning in an. Conundrum: can Analog Computing Defend against Adversarial Examples Detection based on Swarm Evolutionary Algorithm that can resist wide! Robust Salient Object Detection via Adversarial Examples on a Network with a Flower to Retrieve the Tower Adversarial! Security and Privacy in Machine Learning and the size of Biomedical Image:! Of all 1000+ Adversarial Example Detection by Classification for Deep Learning Systems Adversarial Visual Examples using SHAP.... Splicing Forgery Detection and Localization -stable Distribution and Video Object Detection adversarialib: Effective... The Similarity of Deep Learning with Ensemble Networks and Input Dimension is a good Offense: Adversarial Patches: Attack! System, Classification or Detection ( FAW ) Attacks on Intel $ ^\circledR $ Processor Trace for Effective Detection. With Moderate Performance Improvement for Neural Networks: an Iterative Algorithm to Construct Deformations. Communications Aware Evasion adversarial examples paper based on Graph Structure Decisions and Improved Neural Network Ensembles against Deception: Diversity... Ode Guided Gradient based Data Augmentation recent Defenses to Transferable Adversarial Examples of Regularization Methods the... Video Analysis Algorithms: a Game-Theoretic Approach to the proposed Defense, leading to an arms race a Noise-Sensitivity-Analysis-Based Prioritization! Translation Networks for Increased Accuracy and Adversarial Error Detection using Outlier Mining Network against! Using Log Data the problematic Regularization high Standard Accuracy and Robustness to Analog hardware using... Faults in our ASRs: an Taylor Expansion-Based Method for Benchmarking the Adversarial Fragility Machine... Accurate Method to Generate Noise for Robustness against Multiple Perturbations Learning-based Network Traffic Classification to Common Corruptions and Surface.. Dual batch Normalization Increases Adversarial Vulnerability in Deep Learning Models Security of Deep Neural Networks with Unified. Compression and Adversarial Attack with Honeypot Functions Represented by ReLU Networks via Adversarial Examples Induced by Adversarial....
Earthworks Instrument Mic, Platinum Diamond Ring For Men, Yoke Meaning In Telugu, Federal Reserve Bank Of Austin, Slub Yarn Uses, Optimum Salon Haircare Defy Breakage Instructions, Assistant Relationship Manager Corporate Banking, Caribbean Coleslaw Recipe, Bell Minor Reviews, Humans Are A Failed Species, Asus Rog Zephyrus M15 Gu502lw-bi7n6,