Topological Effects on Attacks Against Vertex Classification. Switching Gradient Directions for Query-Efficient Black-Box Adversarial Attacks. Technical Report on the CleverHans v2.1.0 Adversarial Examples Library. A Unified Approach to Interpreting and Boosting Adversarial Transferability. Adversarial Phenomenon in the Eyes of Bayesian Deep Learning. Is Spiking Secure? Entropy Guided Adversarial Model for Weakly Supervised Object Localization. Gradient Regularization Improves Accuracy of Discriminative Models. IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection. Spot Evasion Attacks: Adversarial Examples for License Plate Recognition Systems with Convolutional Neural Networks. DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems. On Physical Adversarial Patches for Object Detection. Robust Deep Reinforcement Learning with Adversarial Attacks. A. K. Jalwana; Mohammed Bennamoun; Ajmal Mian, Xuwang Yin; Soheil Kolouri; Gustavo K. Rohde, Yunhan Jia; Yantao Lu; Junjie Shen; Qi Alfred Chen; Zhenyu Zhong; Tao Wei, Danish Pruthi; Bhuwan Dhingra; Zachary C. Lipton, Haizhong Zheng; Earlence Fernandes; Atul Prakash, Sanjam Garg; Somesh Jha; Saeed Mahloujifar; Mohammad Mahmoody, Shanqing Yu; Jun Zheng; Jinhuan Wang; Jian Zhang; Lihong Chen; Qi Xuan; Jinyin Chen; Dan Zhang; Qingpeng Zhang, Alex Lamb; Jonathan Binas; Anirudh Goyal; Sandeep Subramanian; Ioannis Mitliagkas; Denis Kazakov; Yoshua Bengio; Michael C. Mozer, Daanish Ali Khan; Linhong Li; Ninghao Sha; Zhuoran Liu; Abelino Jimenez; Bhiksha Raj; Rita Singh, Varun Chandrasekaran; Brian Tang; Nicolas Papernot; Kassem Fawaz; Somesh Jha; Xi Wu, Kevin Eykholt; Swati Gupta; Atul Prakash; Amir Rahmati; Pratik Vaishnavi; Haizhong Zheng, Avishek Joey Bose; Andre Cianflone; William L. Hamilton, Jirong Yi; Hui Xie; Leixin Zhou; Xiaodong Wu; Weiyu Xu; Raghuraman Mudumbai, Adam Gleave; Michael Dennis; Cody Wild; Neel Kant; Sergey Levine; Stuart Russell, Tianyu Pang; Kun Xu; Yinpeng Dong; Chao Du; Ning Chen; Jun Zhu, Amir Najafi; Shin-ichi Maeda; Masanori Koyama; Takeru Miyato, Ming Jin; Heng Chang; Wenwu Zhu; Somayeh Sojoudi, Haidar Khan; Daniel Park; Azer Khan; Bülent Yener, Micah Goldblum; Liam Fowl; Soheil Feizi; Tom Goldstein, Zachary Charles; Shashank Rajput; Stephen Wright; Dimitris Papailiopoulos, Ali Shafahi; Parsa Saadatpanah; Chen Zhu; Amin Ghiasi; Christoph Studer; David Jacobs; Tom Goldstein, Yuchi Tian; Ziyuan Zhong; Vicente Ordonez; Gail Kaiser; Baishakhi Ray, Takahiro Itazuri; Yoshihiro Fukuhara; Hirokatsu Kataoka; Shigeo Morishima, Ching-Yun Ko; Zhaoyang Lyu; Tsui-Wei Weng; Luca Daniel; Ngai Wong; Dahua Lin, Chuan Guo; Jacob R. Gardner; Yurong You; Andrew Gordon Wilson; Kilian Q. Weinberger, Bai Li; Changyou Chen; Wenlin Wang; Lawrence Carin, Olga Taran; Shideh Rezaeifar; Taras Holotyak; Slava Voloshynovskiy, Xintian Han; Yuxuan Hu; Luca Foschini; Larry Chinitz; Lior Jankelson; Rajesh Ranganath, Olakunle Ibitoye; Omair Shafiq; Ashraf Matrawy, Mayank Singh; Abhishek Sinha; Nupur Kumari; Harshitha Machiraju; Balaji Krishnamurthy; Vineeth N Balasubramanian, Fuxun Yu; Zhuwei Qin; Chenchen Liu; Liang Zhao; Yanzhi Wang; Xiang Chen, Christian Etmann; Sebastian Lunz; Peter Maass; Carola-Bibiane Schönlieb, Yan Xu; Baoyuan Wu; Fumin Shen; Yanbo Fan; Yong Zhang; Heng Tao Shen; Wei Liu, Shen Wang; Zhengzhang Chen; Jingchao Ni; Xiao Yu; Zhichun Li; Haifeng Chen; Philip S. Yu, Evelyn Duesterwald; Anupama Murthi; Ganesh Venkataraman; Mathieu Sinn; Deepak Vijaykeerthy, Ashkan Khakzar; Shadi Albarqouni; Nassir Navab, Paarth Neekhara; Shehzeen Hussain; Prakhar Pandey; Shlomo Dubnov; Julian McAuley; Farinaz Koushanfar, Yunhan Jia; Yantao Lu; Senem Velipasalar; Zhenyu Zhong; Tao Wei, Saima Sharmin; Priyadarshini Panda; Syed Shakib Sarwar; Chankyu Lee; Wachirawit Ponghiran; Kaushik Roy, Chihye Han; Wonjun Yoon; Gihyun Kwon; Seungkyu Nam; Daeshik Kim, Todor Davchev; Timos Korres; Stathi Fotiadis; Nick Antonopoulos; Subramanian Ramamoorthy, Isaac Dunn; Hadrien Pouget; Tom Melham; Daniel Kroening, Angus Galloway; Anna Golubeva; Thomas Tanay; Medhat Moussa; Graham W. Taylor, Andrew Ilyas; Shibani Santurkar; Dimitris Tsipras; Logan Engstrom; Brandon Tran; Aleksander Madry, Vikash Sehwag; Arjun Nitin Bhagoji; Liwei Song; Chawin Sitawarin; Daniel Cullina; Mung Chiang; Prateek Mittal, Daniel Kang; Yi Sun; Tom Brown; Dan Hendrycks; Jacob Steinhardt, Dinghuai Zhang; Tianyuan Zhang; Yiping Lu; Zhanxing Zhu; Bin Dong, Jinyin Chen; Mengmeng Su; Shijing Shen; Hui Xiong; Haibin Zheng, Yandong Li; Lijun Li; Liqiang Wang; Tong Zhang; Boqing Gong, Wei Ma; Mike Papadakis; Anestis Tsakmalis; Maxime Cordy; Yves Le Traon, Francesco Crecchi; Davide Bacciu; Battista Biggio, Ali Shafahi; Mahyar Najibi; Amin Ghiasi; Zheng Xu; John Dickerson; Christoph Studer; Larry S. Davis; Gavin Taylor; Tom Goldstein, Xiang He; Sibei Yang; Guanbin Li? Mimic and Fool: A Task Agnostic Adversarial Attack. Boosting Adversarial Training with Hypersphere Embedding. On Configurable Defense against Adversarial Example Attacks. Fooling thermal infrared pedestrian detectors in real world using small bulbs. Learnable Boundary Guided Adversarial Training. Generating Black-Box Adversarial Examples in Sparse Domain. Defending Adversarial Examples via DNN Bottleneck Reinforcement. Adversarial Robustness on In- and Out-Distribution Improves Explainability. Feature-Guided Black-Box Safety Testing of Deep Neural Networks. A Formalization of Robustness for Deep Neural Networks. Natural Language Adversarial Attacks and Defenses in Word Level. Attack Graph Convolutional Networks by Adding Fake Nodes. On the Stability of Graph Convolutional Neural Networks under Edge Rewiring. Scalable Inference of Symbolic Adversarial Examples. In this post we'll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult. Security Matters: A Survey on Adversarial Machine Learning. Improving Adversarial Robustness via Guided Complement Entropy. Fine-grained Synthesis of Unrestricted Adversarial Examples. On Visual Hallmarks of Robustness to Adversarial Malware. Metrics and methods for robustness evaluation of neural networks with generative models. (1%). A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees. Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images. Defense Against Adversarial Attacks with Saak Transform. Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability. Improving Query Efficiency of Black-box Adversarial Attack. Defending Adversarial Attacks without Adversarial Attacks in Deep Reinforcement Learning. Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric. Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding. Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent. Explaining Deep Neural Networks Using Spectrum-Based Fault Localization. The potential for adversarial programs to successfully avoid detection and be deployed in black-box settings further highlights the risk … ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models. Adversarial Defense based on Structure-to-Signal Autoencoders. Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. Detecting Adversarial Examples in Convolutional Neural Networks. Adversarial Examples in Deep Learning for Multivariate Time Series Regression. Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection. Sitatapatra: Blocking the Transfer of Adversarial Samples. Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they're like optical illusions for machines. Adversarial Example Generation using Evolutionary Multi-objective Optimization. (98%), Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression. Invariance vs. Robustness of Neural Networks. (76%), Data Poisoning Attacks and Defenses to Crowdsourcing Systems. Regularizers for Single-step Adversarial Training. An Empirical Investigation of Randomized Defenses against Adversarial Attacks. On Saliency Maps and Adversarial Robustness. GNNGuard: Defending Graph Neural Networks against Adversarial Attacks. Query-Efficient Black-Box Attack by Active Learning. Why Botnets Work: Distributed Brute-Force Attacks Need No Synchronization. Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks. Inductive Bias of Gradient Descent based Adversarial Training on Separable Data. Adversarial Robustness through Local Linearization. Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification. Adversarial Neural Pruning with Latent Vulnerability Suppression. Bridging the Performance Gap between FGSM and PGD Adversarial Training. TextAttack: Lessons learned in designing Python frameworks for NLP. Node Copying for Protection Against Graph Neural Network Topology Attacks. ReluDiff: Differential Verification of Deep Neural Networks. Adversarial Defense Through Network Profiling Based Path Extraction. Practical Attacks Against Graph-based Clustering. (80%), TAD: Trigger Approximation based Black-box Trojan Detection for AI. Analysis of Generalizability of Deep Neural Networks Based on the Complexity of Decision Boundary. On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models. Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models. Precise Tradeoffs in Adversarial Training for Linear Regression. An ADMM-Based Universal Framework for Adversarial Attacks on Deep Neural Networks. Beating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions. Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models. Adversarial Attacks on Machine Learning Systems for High-Frequency Trading. Siamese Generative Adversarial Privatizer for Biometric Data. Increased-confidence adversarial examples for improved transferability of Counter-Forensic attacks. Generalizable Adversarial Training via Spectral Normalization. (54%), Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation. Facial Attributes: Accuracy and Adversarial Robustness. Defending Against Adversarial Attacks by Leveraging an Entire GAN. Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles. Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients. Hybrid Batch Attacks: Finding Black-box Adversarial Examples with Limited Queries. Advancing the Research and Development of Assured Artificial Intelligence and Machine Learning Capabilities. (99%), Towards Certifying $\ell_\infty$ Robustness using Neural Networks with $\ell_\infty$-dist Neurons. Effectiveness of Adversarial Examples and Defenses for Malware Classification. Customizing an Adversarial Example Generator with Class-Conditional GANs. On the Sensitivity of Adversarial Robustness to Input Data Distributions. Dynamically Sampled Nonlocal Gradients for Stronger Adversarial Attacks. (99%), Model Agnostic Answer Reranking System for Adversarial Question Answering. Adversarial Attack on Hierarchical Graph Pooling Neural Networks. Block Switching: A Stochastic Approach for Deep Learning Security. Fooling Network Interpretation in Image Classification. Adversarial Attacks on Optimization based Planners. A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles. (1%). Sign-OPT: A Query-Efficient Hard-label Adversarial Attack. Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness. Exploiting vulnerabilities of deep neural networks for privacy protection. Towards Imperceptible Adversarial Image Patches Based on Network Explanations. Fuzzy Unique Image Transformation: Defense Against Adversarial Attacks On Deep COVID-19 Models. Adversarial Profiles: Detecting Out-Distribution & Adversarial Samples in Pre-trained CNNs. Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment. Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions. On the Limitation of Convolutional Neural Networks in Recognizing Negative Images. Robustness properties of Facebook's ResNeXt WSL models. ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio. LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks. Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks. Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks. DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks. Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices. Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers. Below here we have listed down the top 12 research papers on adversarial learning presented at Computer Vision and Pattern Recognition Conference. Defending Against Multiple and Unforeseen Adversarial Videos. Adversarial Examples for Cost-Sensitive Classifiers. Practical Black-Box Attacks against Machine Learning. Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer. Generalizing Universal Adversarial Attacks Beyond Additive Perturbations. Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles. Practical Fast Gradient Sign Attack against Mammographic Image Classifier. Robust Physical-World Attacks on Deep Learning Models. Stars. Label Smoothing and Adversarial Robustness. Making Images Undiscoverable from Co-Saliency Detection. Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers. In this category, the attacker focus on a face recognition system (like Face++), to make the classifier misclassify the input face or cannot detect faces. On Adversarial Examples for Character-Level Neural Machine Translation. PAC-learning in the presence of evasion adversaries. Weighted Average Precision: Adversarial Example Detection in the Visual Perception of Autonomous Vehicles. Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on Bitcoin. Defending Against Adversarial Machine Learning. TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP. Robust or Private? Adversarial Feature Selection against Evasion Attacks. Test Metrics for Recurrent Neural Networks. Fake News Detection via NLP is Vulnerable to Adversarial Attacks. Robustness of Rotation-Equivariant Networks to Adversarial Perturbations. EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. A Partial Break of the Honeypots Defense to Catch Adversarial Attacks. SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters. Towards Understanding Adversarial Examples Systematically: Exploring Data Size, Task and Model Factors. Manifold Preserving Adversarial Learning. Evaluating Adversarial Robustness for Deep Neural Network Interpretability using fMRI Decoding. A Game Theoretic Analysis of LQG Control under Adversarial Attack. Challenging the adversarial robustness of DNNs based on error-correcting output codes. Towards Transferable Adversarial Attack against Deep Face Recognition. Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness. Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning. Adversarial Binaries for Authorship Identification. Unifying Model Explainability and Robustness via Machine-Checkable Concepts. A Simple Explanation for the Existence of Adversarial Examples with Small Hamming Distance. Towards an Understanding of Neural Networks in Natural-Image Spaces. Attacking Convolutional Neural Network using Differential Evolution. Verifying the Causes of Adversarial Examples. Adversarial Distributional Training for Robust Deep Learning. Adversarial Attacks and Defenses: An Interpretation Perspective. Adversarial Attacks in Sound Event Classification. Verification of Deep Convolutional Neural Networks Using ImageStars. Graph Adversarial Learning. Automatic Detection of Generated Text is Easiest when Humans are Fooled. Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations. An Empirical Study on the Robustness of NAS based Architectures. Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data. Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey. Robust Encodings: A Framework for Combating Adversarial Typos. Black-box Adversarial Sample Generation Based on Differential Evolution. RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies. Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization. Feature-level Malware Obfuscation in Deep Learning. Towards Robust Toxic Content Classification. Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable. Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks. Explaining Black-box Android Malware Detection. Papers With Code highlights trending Machine Learning research and the code to implement it. Adversarial Examples: Opportunities and Challenges. Provable Robustness of ReLU networks via Maximization of Linear Regions. Is Deep Learning Safe for Robot Vision? Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup. Stochastic Security: Adversarial Defense Using Long-Run Dynamics of Energy-Based Models. When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers. A Critical Evaluation of Open-World Machine Learning. Enhanced Regularizers for Attributional Robustness. MAAC: Novel Alert Correlation Method To Detect Multi-step Attack. Divide, Denoise, and Defend against Adversarial Attacks. A Cyclically-Trained Adversarial Network for Invariant Representation Learning. The only requirement I used for selecting papers for this list I-GCN: Robust Graph Convolutional Network via Influence Mechanism. VarMixup: Exploiting the Latent Space for Robust Training and Inference. Why Blocking Targeted Adversarial Perturbations Impairs the Ability to Learn. Overfitting in adversarially robust deep learning. DeepFault: Fault Localization for Deep Neural Networks. Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations. Vulnerability Under Adversarial Machine Learning: Bias or Variance? Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness. Does Symbolic Knowledge Prevent Adversarial Fooling? Principal Component Properties of Adversarial Samples. Towards Understanding Fast Adversarial Training. Adversarial Examples for Semantic Segmentation and Object Detection. Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks. An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms. Adversarial Robustness Against Image Color Transformation within Parametric Filter Space. Residual Networks as Nonlinear Systems: Stability Analysis using Linearization. Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network. Adversarial Attack on Facial Recognition using Visible Light. Heat and Blur: An Effective and Fast Defense Against Adversarial Examples. The Human Visual System and Adversarial AI. Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability. Adversarial Momentum-Contrastive Pre-Training. Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems. Improving Resistance to Adversarial Deformations by Regularizing Gradients. Trojaning Language Models for Fun and Profit. RAID: Randomized Adversarial-Input Detection for Neural Networks. Robustness Verification of Support Vector Machines. WITCHcraft: Efficient PGD attacks with random step size. Adversarial Threats to DeepFake Detection: A Practical Perspective. Efficient and Transferable Adversarial Examples from Bayesian Neural Networks. written each year. (1%), Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity. Contrastive Learning with Adversarial Perturbations for Conditional Text Generation. A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks. Anomalous Instance Detection in Deep Learning: A Survey. Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. (1%), Adversarial defense for automatic speaker verification by cascaded self-supervised learning models. Bluff: Interactively Deciphering Adversarial Attacks on Deep Neural Networks. Machine vs Machine: Minimax-Optimal Defense Against Adversarial Examples. CG-ATTACK: Modeling the Conditional Distribution of Adversarial Perturbations to Boost Black-Box Attack. Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks. Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers? Fundamental Tradeoffs in Distributionally Adversarial Training. HAWKEYE: Adversarial Example Detector for Deep Neural Networks. Exploring the Robustness of NMT Systems to Nonsensical Inputs.
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