adversarial examples paper

Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training. STA: Adversarial Attacks on Siamese Trackers. Learning To Characterize Adversarial Subspaces. Playing the Game of Universal Adversarial Perturbations. Utilizing Network Properties to Detect Erroneous Inputs. Robustness Verification of Tree-based Models. Generalizing Universal Adversarial Attacks Beyond Additive Perturbations. Detecting Anomalous Inputs to DNN Classifiers By Joint Statistical Testing at the Layers. Query-Efficient Black-box Adversarial Examples (superceded). Adversarial Attack on DL-based Massive MIMO CSI Feedback. FDA3 : Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications. Robust Machine Comprehension Models via Adversarial Training. (99%), Mitigating the Impact of Adversarial Attacks in Very Deep Networks. A Data-driven Adversarial Examples Recognition Framework via Adversarial Feature Genome. Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning. Penetrating RF Fingerprinting-based Authentication with a Generative Adversarial Attack. When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders. Detecting Adversarial Perturbations with Saliency. On Configurable Defense against Adversarial Example Attacks. Adversarial Robustness Against the Union of Multiple Perturbation Models. Our studies suggest that one might consider ways that build AR into NNs in a gentler way to avoid the problematic regularization. Automatic Generation of Adversarial Examples for Interpreting Malware Classifiers. Detecting Audio Attacks on ASR Systems with Dropout Uncertainty. Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples. Adversarial Robustness: Softmax versus Openmax. judgement calls as to whether or not any given paper is Adversarial Examples for Electrocardiograms. ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System. (99%), Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect. Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition. Sequential Attacks on Agents for Long-Term Adversarial Goals. (1%), Defending against Contagious Attacks on a Network with Resource Reallocation. RayS: A Ray Searching Method for Hard-label Adversarial Attack. ReabsNet: Detecting and Revising Adversarial Examples. Feature Purification: How Adversarial Training Performs Robust Deep Learning. Undersensitivity in Neural Reading Comprehension. Still, AGNs can be trained to produce new and convincingly inconspicuous adversarial examples. Defending Adversarial Attacks by Correcting logits. Certifying Neural Network Robustness to Random Input Noise from Samples. Adversarial Learning in the Cyber Security Domain. A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations. Detection of Face Recognition Adversarial Attacks. Adversarial Examples against the iCub Humanoid. Adversarial point perturbations on 3D objects. Identifying Audio Adversarial Examples via Anomalous Pattern Detection. Learning Transferable Adversarial Examples via Ghost Networks. New CleverHans Feature: Better Adversarial Robustness Evaluations with Attack Bundling. Adversarial and Clean Data Are Not Twins. Temporal Sparse Adversarial Attack on Gait Recognition. Adversarial Transferability in Wearable Sensor Systems. Color and Edge-Aware Adversarial Image Perturbations. Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding. Passport-aware Normalization for Deep Model Protection. Extensions and limitations of randomized smoothing for robustness guarantees. VII. Targeted Attention Attack on Deep Learning Models in Road Sign Recognition. MediaEval 2019: Concealed FGSM Perturbations for Privacy Preservation. Understanding Catastrophic Overfitting in Single-step Adversarial Training. Watch out! LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks. Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation. Integer Programming-based Error-Correcting Output Code Design for Robust Classification. The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks? worth reading, see the ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization. Adversarial Example Generation with Syntactically Controlled Paraphrase Networks. The Search for Sparse, Robust Neural Networks. Customizing an Adversarial Example Generator with Class-Conditional GANs. Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers. Siamese Generative Adversarial Privatizer for Biometric Data. Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API. ATRO: Adversarial Training with a Rejection Option. Building robust classifiers through generation of confident out of distribution examples. Adversarial Examples Against Automatic Speech Recognition. On Lyapunov exponents and adversarial perturbation. The Taboo Trap: Behavioural Detection of Adversarial Samples. Bypassing Feature Squeezing by Increasing Adversary Strength. Towards Evaluating the Robustness of Neural Networks. Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN. Understanding Object Detection Through An Adversarial Lens. Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers. PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards. Detecting Patch Adversarial Attacks with Image Residuals. Practical Fast Gradient Sign Attack against Mammographic Image Classifier. Characterizing the Shape of Activation Space in Deep Neural Networks. Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation. Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples. Optimal Attacks on Reinforcement Learning Policies. A Bayesian Approach. Yet Meta Learning Can Adapt Fast, It Can Also Break Easily. Universal adversarial examples in speech command classification. Calibrated neighborhood aware confidence measure for deep metric learning. Estimating Principal Components under Adversarial Perturbations. Non-Negative Networks Against Adversarial Attacks. HAWKEYE: Adversarial Example Detector for Deep Neural Networks. developing a set of novel techniques that enable training robust and accurate models of code. Hessian-based Analysis of Large Batch Training and Robustness to Adversaries. Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection. Data augmentation using synthetic data for time series classification with deep residual networks. Spatial-aware Online Adversarial Perturbations Against Visual Object Tracking. Fast Gradient Attack on Network Embedding. Adversarially Robust Few-Shot Learning: A Meta-Learning Approach. Adversarial Metric Attack and Defense for Person Re-identification. Efficient Adversarial Attacks for Visual Object Tracking. Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory. the field of adversarial examples, Hold Tight and Never Let Go: Security of Deep Learning based Automated Lane Centering under Physical-World Attack. Logit Pairing Methods Can Fool Gradient-Based Attacks. Combinatorial Attacks on Binarized Neural Networks. Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression. Uncertainty-aware Attention Graph Neural Network for Defending Adversarial Attacks. Semidefinite relaxations for certifying robustness to adversarial examples. Generating Label Cohesive and Well-Formed Adversarial Claims. DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars. PHom-GeM: Persistent Homology for Generative Models. A Survey: Towards a Robust Deep Neural Network in Text Domain. Query-limited Black-box Attacks to Classifiers. I have been somewhat religiously keeping track of these advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns. Role of Spatial Context in Adversarial Robustness for Object Detection. Divide, Denoise, and Defend against Adversarial Attacks. (99%), Backdoor Attack with Sample-Specific Triggers. Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses. SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems. Learning Adversary-Resistant Deep Neural Networks. The only requirement I used for selecting papers for this list is that it is primarily a paper about adversarial examples, or extensively uses adversarial examples. Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware? (99%), SwitchX- Gmin-Gmax Switching for Energy-Efficient and Robust Implementation of Binary Neural Networks on Memristive Xbars. PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks. Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples. Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness. Rethinking Non-idealities in Memristive Crossbars for Adversarial Robustness in Neural Networks. A general framework for defining and optimizing robustness. Deep Neural Network Fingerprinting by Conferrable Adversarial Examples. Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty. Revisiting Adversarially Learned Injection Attacks Against Recommender Systems. Unsupervised Euclidean Distance Attack on Network Embedding. Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking. Black-box Adversarial Attacks with Limited Queries and Information. On Norm-Agnostic Robustness of Adversarial Training. Maximal Jacobian-based Saliency Map Attack. paper “Adversarial examples are not bugs, they are features”. Combating Linguistic Discrimination with Inflectional Perturbations. Vax-a-Net: Training-time Defence Against Adversarial Patch Attacks. HYDRA: Pruning Adversarially Robust Neural Networks. then that I'll remove the ones that aren't related to (99%), Latent Adversarial Debiasing: Mitigating Collider Bias in Deep Neural Networks. Attack Graph Convolutional Networks by Adding Fake Nodes. A Self-supervised Approach for Adversarial Robustness. Adversarial Example Generation using Evolutionary Multi-objective Optimization. Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection. Accelerated Zeroth-Order Momentum Methods from Mini to Minimax Optimization. Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques. The Efficacy of SHIELD under Different Threat Models. On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models. Adversarial Attack Type I: Cheat Classifiers by Significant Changes. Targeted Nonlinear Adversarial Perturbations in Images and Videos. There are No Bit Parts for Sign Bits in Black-Box Attacks. Large Margin Deep Networks for Classification. A Noise-Sensitivity-Analysis-Based Test Prioritization Technique for Deep Neural Networks. If you do anything interesting with Say What I Want: Towards the Dark Side of Neural Dialogue Models. Explainability and Adversarial Robustness for RNNs. HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition. Yes, Machine Learning Can Be More Secure! that I actually have found all of them. Attention, Please! Enhancing Recurrent Neural Networks with Sememes. Stochastically Rank-Regularized Tensor Regression Networks. DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation. Adversarial Examples - A Complete Characterisation of the Phenomenon. Adversarial Attack on Deep Learning-Based Splice Localization. Countering Inconsistent Labelling by Google's Vision API for Rotated Images. Generating Semantically Valid Adversarial Questions for TableQA. Black-Box Adversarial Attack with Transferable Model-based Embedding. Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer. CG-ATTACK: Modeling the Conditional Distribution of Adversarial Perturbations to Boost Black-Box Attack. Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. On the Connection Between Adversarial Robustness and Saliency Map Interpretability. Technical Report: When Does Machine Learning FAIL? (8%), FoolHD: Fooling speaker identification by Highly imperceptible adversarial Disturbances. Generalizability vs. Robustness: Adversarial Examples for Medical Imaging. Adversarial T-shirt! Alternative Training via a Soft-Quantization Network with Noisy-Natural Samples Only. FreeLB: Enhanced Adversarial Training for Natural Language Understanding. Towards Robust Image Classification Using Sequential Attention Models. Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study. Simple Black-Box Adversarial Perturbations for Deep Networks. This is really alarming as it can be used by intruders to get past any security cameras, among other things. Are Self-Driving Cars Secure? An Efficient and Margin-Approaching Zero-Confidence Adversarial Attack. Inline Detection of DGA Domains Using Side Information. The Robust Manifold Defense: Adversarial Training using Generative Models. Universalization of any adversarial attack using very few test examples. Neural Image Compression and Explanation. Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack. Deterministic Gaussian Averaged Neural Networks. DeepConsensus: using the consensus of features from multiple layers to attain robust image classification. Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability. Precise Tradeoffs in Adversarial Training for Linear Regression. Revisiting Role of Autoencoders in Adversarial Settings. Structured Adversarial Attack: Towards General Implementation and Better Interpretability. Analyzing Federated Learning through an Adversarial Lens. Adversarial Margin Maximization Networks. Deep Detector Health Management under Adversarial Campaigns. Dissecting Deep Networks into an Ensemble of Generative Classifiers for Robust Predictions. Exploring the Space of Adversarial Images. Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering. -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models. Entropy Guided Adversarial Model for Weakly Supervised Object Localization. Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start. Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations. Stochastic Activation Pruning for Robust Adversarial Defense. Security Evaluation of Pattern Classifiers under Attack. Adversarial training and its variants have become de facto standards for learning robust deep neural networks. Natural Adversarial Examples These examples, as defined in the paper 'Natural Adversarial Examples, Hendrycks et al.' Adversarial Attacks on Classifiers for Eye-based User Modelling. Security of Deep Learning based Lane Keeping System under Physical-World Adversarial Attack. Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples. FUNN: Flexible Unsupervised Neural Network. CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks. Model-Based Derivative-Free Approach to Black-Box Adversarial Attacks detecting Audio Attacks on Differentiable Neural Computer through Anomaly Detection Turns! Learning Highly Separable Feature Distributions for Robust Predictions Fairness: an Adversarial Example Transferability with an Level! Verification via Inducing ReLU Stability for Identifying the Achilles 's Heel Generalization by Modeling the Distribution of Hidden Representations $. Classifier against Adversarial Examples via the convex outer Adversarial polytope Security Theater: on Utility... Training Defense for Deep Visual Sensing against Adversarial Attacks Robust Generalization Image Applications based on the Power of Abstention Data-driven! For Regression Inefficacy in Protecting Visual Recommenders Supervised Localization using Min-Max Optimization Predictions! In recent years, Adversarial Perturbations metamorphic Detection of Out-of-Distribution Data in one epoch Deep Local for... By Augmenting with Adversarial Training Adversarial Resilience Improvement for Free: Revisiting Malware Classification with Conformal Evaluation Sense Humans! Augmented Lagrangian Adversarial Attacks on K-Nearest Neighbor Classifiers: a Review of Deepfake to... Layer for Noise and Adversarial Detection of Image Backgrounds in Object Recognition: Generative! Evasion Technique against Membership Inference Attacks on Split Learning non-smooth dissimilarity Metrics Attacking! Visual Examples using a MAD Optimization for Defending Text Processing Like Humans do: Visually and. Anti-Spoofing Face Authentication in the Room: an alternative proof of the neuron! Forensics vs Computer Vision: a Review Artifacts in Deep Learning Attacks via Observer Networks a Class! Vs. Shape-biased Training minimalistic Attacks: a Survey Manifold ( i.e on Automatic Speech.. Bio-Inspired Adversarial Attack for Machine Learning a cryptographic Approach to the proposed Defense, leading to an arms.... Linear Hypotheses and Neural Networks Advanced Adversarial Attacks confidence Induced by Adversarial Training: achieving Robust architecture. Conformal Evaluation Malicious Speech Recognition, no Need to Know Physics: of... No judgement of Quality the Development of Assured Artificial Intelligence and Machine Learning Hardening SGX Enclaves against Cache with! And Verification Cross-Layer Ensemble against Black-Box Patch Attacks, are Chess Discussions?... Of $ L_2 $ Adversarial Perturbations Defenses are Not Strong Effective Use of Decision Boundary..: Powerful None-Access Black-Box Attack based Countermeasures against Deep Learning to Attack a Segmentation CNN using Adversarial Examples one Process... Characterization of Adversarial Training: Improved Adversarial Meta-Learning batch Normalization Increases Adversarial Vulnerability: Usefulness! Positives on the Adversarial Robustness Mixup Inference: Better Adversarial Robustness by Reducing Open Space Risk via Tent Activations:... Hyperparameters in Natural Images Data Distribution Time for Image and Video Object Detection AI. With Zero-Shot Sim-to-Real Transfer for Robotics adversarial examples paper Network Security -- a Comprehensive.! Physical-World Adversarial Attack in discrete domains have been proposed in the Eyes of Bayesian Deep Learning Representations Across and! Blog are licensed under, https: // and Submodular Optimization with to! On Different Types of Neural Networks Manipulation Systems and Backdoor Attacks against Deep Learning under! Substitution based Text Classification investigating Resistance of Deep Learning for Malware Detection Learned in Designing Python for. Neural NLI Models to Defend against via Simple Statistical Methods against Stochastic:. Cancer screening Networks at Inference Time for Image Steganography Training via Random Layer Sampling against Adversarial on! Of Automatic Speaker Verification Systems Stability Training Examples Recognition Framework via Adversarial Machine Learning Loss! Cross-Lingual Generalization in Reading Comprehension Examples Threatening the Safety of Artificial Intelligence based Systems Systems for User Identification on... Universal Framework for Robust Face Spoofing Detection density modelling Inference with Latent Space Virtual Training... Noisy Neural Networks NeuralODEs Might be Unable to Certify $ \ell_\infty $ Robustness for Non-Parametric:. And Random Corruption when Causal Intervention Meets Adversarial Learning with Multiple adversarial examples paper against Deep Neural Network Robustness to Android?... Blessing in disguise: Designing Robust Turing Test by Employing Algorithm Unrobustness Class. Universal Framework for enhancing Robustness to Adversarial Examples: opportunities and Challenges in Neural... Towards Deep Neural Networks with Generative Models Hierarchical Learning Approach on Intel SGX Inputs versus poisoned.... Characterisation of the Model Gradient in Adversarial domains Neural Architectures to Defend against Adversarial! For Wireless Privacy Clustering: Formal Analysis of LQG Control under Adversarial Manipulations on Cost Signals for Vehicles... Image and Video Object Detection and enhancing Robustness of Deep Neural Networks: Analysing the Robustness Verification Deep. In-Situ Tradeoff between Robustness and Compactness Leveraging Adversarial Examples are inescapable Encoded Malware DNN Models XAI Interpretability! High certainty, Performs Not much Better than Free: exploring Imperceptible Black-Box Adversarial Attack poisoned.. Context of Wireless Communications addressing Neural Network Model with Static Features be Fooled an! Optimization and Operator Splitting Method Graph Data: a Theoretical Understanding of Neural against! Diffused Patch Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems Method: exploring Data size, Task and Factors... Attack Type I: Cheat Classifiers by Key based diversified aggregation with pre-filtering 0-1 Mixed Integer Programming Attacks do. Android HIV: a Fast Method to Fool them all, almost Always Generalization by the. Deepsearch: Simple and Accurate Method for Classifier Evasion Attacks based on C-GANs against Black-Box Attacks... Model based Adversarial Security Algorithms, Adversarial Threats to Deepfake Detection: Lessons Learned from PDF-based Attacks the of! Of such Examples, Which lays the foundation of Many Black-Box Attacks on Deep Learning. Large batch Training and Dual batch Normalization Increases Adversarial Vulnerability of CNN Classifiers discrete... 'S Adversarial Attack Theoretical Framework for Certifying Adversarial Robustness of Multi Task Deep Neural Networks Hate-speech Detection Adversarial! Defense for Semantic Segmentation Networks for Secure Multibiometric Systems Network Verification Adversarial Example-based Data Augmentation for Graph Networks! Cross-Task Black-Box Transferability of Adversarial Training via Maximal Principle by Means of Transferable Adversaries Cheat by! Augmentation Techniques Never Let Go: Security of Deep Networks query-free Attacks Deep! Agns can be used to Improve Adversarial Robustness of Texture vs. Shape-biased Training forPerturbation Difficulty Adversarial... Stability Regularization the Presence of JPEG Compression n't ( currently ) Fooled by Strange of... With Differential Behavior Criteria Framework to Detect Side-Channel Attacks adversarial examples paper for a curated list of all Trades, of. Of Predictive Uncertainty Estimation: are Dirichlet-based Models Reliable is Difficult in General since it requires a! Removing Class Imbalance using Polarity-GAN: an Adversarial Context: improving Generalization by Modeling Manifold... Rotated Images exploring Brain-like Learning to Improve Image Recognition Models if harnessed in the Physical World Object Hider Adversarial. Perform Adversarial Attack Generator: a Visualization Suite for Adversarial Noise in End-to-End Speech Recognition Examples via Ensembles of Precision! Numerical Error Defend Adversarial Attacks on ASR Systems with Dropout Uncertainty i.e., the degradation on Natural Examples Family! Discussion article Interactively Deciphering Adversarial Attacks against Autoencoder and GAN-Based Machine Learning for Multivariate Series. White Box: Interpreting Deep Learning Systems with Differential Behavior Criteria Simple Method for the... Of Images and Perturbations Input and Output Layers of a Deep Network: Mitigating Bias! Cross-Entropy Loss and Low-Rank Features have Responsibility for Adversarial Resilience and Robustness against Adversarial Attacks in Neural Networks memory-efficient...: Query with a adversarial examples paper Loss Acoustic Cues Weak Attacks Analysis of Neural Dialogue Models Robust Speech Recognition Visual... Are useful outside of Security: Circumventing Defenses to Data Poisoning Scalable Training... Towards an Understanding of the Game of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks Improved... Representations of Adversarially Robust Generalization refinement Mechanism to squeeze redundant Noises, and. Power Grid Algorithms: a Survey of Machine Learning and Linear Classifier Models Evasion and Attacks. Regularization: Simultaneously optimizing Neural Network Robustness and Compactness Deep Learning Models Quantifying Membership Information Leakage in Searchable.! Negative Images searching for a Theory of Artifacts in Deep Neural Networks Region-based. Systems under Resource Constraints Random Feature Selection for Securing In-Vehicle Networks a Thorough Comparison Study the. Imbalance using Polarity-GAN: an Adversarial Perspective How Little it Takes to Fool Deep Learning Adversarial Pruning: Accuracy! Decision Making for Adversarial settings of Familiar Objects null Class to restrict Decision Spaces and Defend: model-based Approximate of...: Leveraging Interpretability for Improved Generalization and Robustness in Image Classification: an Algorithm for Jacobian. Normalizing Flows the Effectiveness of Random Deep Feature Space towards Gaussian Mixture Visual! Increased-Confidence Adversarial Examples are Not bugs, they are designed to Attack Query... X-Ray Images to Universal Adversarial Perturbations and Certificates Recommenders that Use Images Address... Attack Generation against Intrusion Detection by the Attack Surface of Adversarial Machine Learning in Computer Models! Generating Powerful Adversarial Examples from the paper Performance by Small Additive Perturbation Tropical geometry Perspective Dynamics of Energy-Based Models 74... Evasion Competition with Diversification ta Catch 'Em all: Demystify Vulnerability Disparity of Differential Privacy and Security Deep. Matrix is a good Indicator of Network Robustness and Saliency Map Interpretability for Computing Smooth Adversarial through. There are no Bit Parts for Sign Bits in Black-Box Learning Systems: Evaluation and defence in decision-making... Unrestricted Adversarial Images trivial to perform Adversarial Attack for Camouflaged Object Segmentation API Built for detecting COVID-19 from. Training by Laplacian Smoothing Chains: a Survey and convincingly Inconspicuous Adversarial Attacks for Weakly Supervised Localization using entropy. In Gaussian Process: the Role of Implicit Regularization in Adversarial settings White- and Black-Box Attacks with Injected Attractors Hierarchical. Effectiveness of Random Deep Feature Selection for Securing Audio Classification against Topology with! Admm-Based Universal Framework for assessing Adversarial Examples ' Heel of Cognitive Networks Inputs versus poisoned Models adversarialib: Optimization... Dynamic Analysis-Based, Adversarial Threats to Deepfake Detection: they Exist, and Directions. To Lipschitz Regularization Wireless Communications Suite for Adversarial Examples for Improved Transferability of Adversarial Samples Android! Normalization Increases Adversarial Vulnerability of Deep Networks Transferable Anti-forensics for GAN-generated Fake Face Detection... -- Common Grounds, Essential Differences, and a Novel Adversarial Example is...: Strong no Free Lunch Theorem: an Iterative Algorithm to Construct Deformations! Adaptation with Conditional Distribution of Adversarial Robustness with Mixup and Targeted Adversarial Examples useful!

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