NeurIPS16~19のadversarial examples関連論文リンク集
目視で判断したので、間違っていたり抜けてたりするかもしれませんが、ご容赦ください。
19
[1910.07629] A New Defense Against Adversarial Images: Turning a Weakness into a Strength
Adversarial Examples are not Bugs, they are Features
[1906.04948] Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
[1904.12843] Adversarial Training for Free!
[1910.14356] Certifiable Robustness to Graph Perturbations
[1809.03113] Certified Adversarial Robustness with Additive Noise
[1905.12202] Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
[1907.10764] Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training
[1906.04392] Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks
[1811.10745] ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
[1910.06513] ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
[1902.03538] Model Compression with Adversarial Robustness: A Unified Optimization Framework
[1905.00877] You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
[1905.12784] Intrinsic dimension of data representations in deep neural networks
[1902.02041] Fooling Neural Network Interpretations via Adversarial Model Manipulation
[1908.01517] Adversarial Self-Defense for Cycle-Consistent GANs
[1909.05822] On the Hardness of Robust Classification
[1905.13021] Robustness to Adversarial Perturbations in Learning from Incomplete Data
[1905.13736] Unlabeled Data Improves Adversarial Robustness
[1909.00900] Metric Learning for Adversarial Robustness
Cross-Modal Learning with Adversarial Samples
[1904.13000] Adversarial Training and Robustness for Multiple Perturbations
[1911.00126] Adversarial Music: Real World Audio Adversary Against Wake-word Detection System
[1906.04584] Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
[1905.09768] Zero-shot Knowledge Transfer via Adversarial Belief Matching
[1907.02610] Adversarial Robustness through Local Linearization
[1905.13725] Are Labels Required for Improving Adversarial Robustness?
[1906.06919] Improving Black-box Adversarial Attacks with a Transfer-based Prior
[1906.00001] Functional Adversarial Attacks
[1905.11736] Cross-Domain Transferability of Adversarial Perturbations
Certifying Geometric Robustness of Neural Networks
[1906.03526] Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks
[1910.05821] Policy Poisoning in Batch Reinforcement Learning and Control
[1911.04681] On Robustness to Adversarial Examples and Polynomial Optimization
[1905.09027] Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder
[1906.03849] Robustness Verification of Tree-based Models
[1905.09957] Robust Attribution Regularization
[1909.12272] Lower Bounds on Adversarial Robustness from Optimal Transport
[1906.07916] Convergence of Adversarial Training in Overparametrized Neural Networks
[1902.01148] Theoretical evidence for adversarial robustness through randomization
On Relating Explanations and Adversarial Examples
Image Synthesis with a Single (Robust) Classifier
18
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
Deep Defense: Training DNNs with Improved Adversarial Robustness
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
With Friends Like These, Who Needs Adversaries?
Scaling provable adversarial defenses
Thwarting Adversarial Examples: An $L_0$-Robust Sparse Fourier Transform
A Spectral View of Adversarially Robust Features
Adversarially Robust Generalization Requires More Data
Towards Robust Detection of Adversarial Examples
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
Towards Robust Interpretability with Self-Explaining Neural Networks
Semidefinite relaxations for certifying robustness to adversarial examples
Adversarial Attacks on Stochastic Bandits
Adversarial vulnerability for any classifier
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution
Constructing Unrestricted Adversarial Examples with Generative Models
Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks
Sparse DNNs with Improved Adversarial Robustness
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
17
Certified Defenses for Data Poisoning Attacks
Lower bounds on the robustness to adversarial perturbations
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples
[1712.09665] Adversarial Patch
16
ECCV18, 20のadversarial examples関連論文リンク集
目視で判断したので、間違っていたり抜けてたりするかもしれませんが、ご容赦ください。
20
Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot learning
Regularization with Latent Space Virtual Adversarial Training
Targeted Attack for Deep Hashing based Retrieval
Multitask Learning Strengthens Adversarial Robustness
Towards Automated Testing and Robustification by Semantic Adversarial Data Generation
Improved Adversarial Training via Learned Optimizer
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Indirect Local Attacks for Context-aware Semantic Segmentation Networks
AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds
Adversarial T-shirt! Evading Person Detectors in A Physical World
Bias-based Universal Adversarial Patch Attack for Automatic Check-out
SemanticAdv: Generating Adversarial Examples via Attribute-conditioned Image Editing
Adversarial Ranking Attack and Defense
Yet Another Intermediate-Level Attack
Boosting Decision-based Black-box Adversarial Attacks with Random Sign Flip
Spatiotemporal Attacks for Embodied Agents
Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering
Robust Tracking against Adversarial Attacks
Sparse Adversarial Attack via Perturbation Factorization
Square Attack: a query-efficient black-box adversarial attack via random search
Improving Query Efficiency of Black-box Adversarial Attack
What makes fake images detectable? Understanding properties that generalize
Efficient Adversarial Attacks for Visual Object Tracking
PatchAttack: A Black-box Texture-based Attack with Reinforcement Learning
Practical Poisoning Attacks on Neural Networks
Improving Adversarial Robustness by Enforcing Local and Global Compactness
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
Patch-wise Attack for Fooling Deep Neural Network
Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds
Manifold Projection for Adversarial Defense on Face Recognition
New Threats against Object Detector with Non-local Block
18
Practical Black-box Attacks on Deep Neural Networks using Efficient Query Mechanisms
Ask, Acquire and Attack: Data-free UAP generation using Class impressions
Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization
Learning Discriminative Video Representations Using Adversarial Perturbations
ICCV17, 19のadversarial examples関連論文リンク集
目視で判断したので、間違っていたり抜けてたりするかもしれませんが、ご容赦ください。
19
Adversarial Robustness vs. Model Compression, or Both?
Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks
Towards Adversarially Robust Object Detection
Generative Adversarial Minority Oversampling
DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense
Fooling Network Interpretation in Image Classification
SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition
Adversarial Defense via Learning to Generate Diverse Attacks
Universal Adversarial Perturbation via Prior Driven Uncertainty Approximation
Understanding Deep Networks via Extremal Perturbations and Smooth Masks
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos
Why Does a Visual Question Have Different Answers?
Sparse and Imperceivable Adversarial Attacks
Enhancing Adversarial Example Transferability With an Intermediate Level Attack
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
Hilbert-Based Generative Defense for Adversarial Examples
Physical Adversarial Textures That Fool Visual Object Tracking
The LogBarrier Adversarial Attack: Making Effective Use of Decision Boundary Information
Improving Adversarial Robustness via Guided Complement Entropy
Universal Perturbation Attack Against Image Retrieval
Defending Against Universal Perturbations With Shared Adversarial Training
Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
Targeted Mismatch Adversarial Attack: Query With a Flower to Retrieve the Tower
FDA: Feature Disruptive Attack
17
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly
Adversarial Examples for Semantic Segmentation and Object Detection
Adversarial Image Perturbation for Privacy Protection -- A Game Theory Perspective
Interpretable Explanations of Black Boxes by Meaningful Perturbation
Universal Adversarial Perturbations Against Semantic Image Segmentation
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
Adversarial Examples Detection in Deep Networks With Convolutional Filter Statistics
Adversarial Examples論文リンク集
ICLR14~21のadversarial examples関連論文リンク集
目視で判断したので、間違っていたり抜けてたりするかもしれませんが、ご容赦ください。
21
Geometry-aware Instance-reweighted Adversarial Training | OpenReview
Improving Adversarial Robustness via Channel-wise Activation Suppressing | OpenReview
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples | OpenReview
How Benign is Benign Overfitting ? | OpenReview
Stabilized Medical Image Attacks | OpenReview
Learning perturbation sets for robust machine learning | OpenReview
Removing Undesirable Feature Contributions Using Out-of-Distribution Data | OpenReview
Robust Reinforcement Learning on State Observations with Learned Optimal Adversary | OpenReview
Efficient Certified Defenses Against Patch Attacks on Image Classifiers | OpenReview
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits | OpenReview
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation | OpenReview
Understanding the failure modes of out-of-distribution generalization | OpenReview
Effective and Efficient Vote Attack on Capsule Networks | OpenReview
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation | OpenReview
Bag of Tricks for Adversarial Training | OpenReview
Policy-Driven Attack: Learning to Query for Hard-label Black-box Adversarial Examples | OpenReview
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models | OpenReview
Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks | OpenReview
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning | OpenReview
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification | OpenReview
Online Adversarial Purification based on Self-supervised Learning | OpenReview
Self-supervised Adversarial Robustness for the Low-label, High-data Regime | OpenReview
Robust Overfitting may be mitigated by properly learned smoothening | OpenReview
WaNet - Imperceptible Warping-based Backdoor Attack | OpenReview
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity | OpenReview
Fooling a Complete Neural Network Verifier | OpenReview
20
ICLR: Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
ICLR: Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
ICLR: Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
ICLR: FreeLB: Enhanced Adversarial Training for Natural Language Understanding
ICLR: Empirical Studies on the Properties of Linear Regions in Deep Neural Networks
ICLR: Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
ICLR: Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
ICLR: Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$
ICLR: Jacobian Adversarially Regularized Networks for Robustness
ICLR: Implicit Bias of Gradient Descent based Adversarial Training on Separable Data
ICLR: MMA Training: Direct Input Space Margin Maximization through Adversarial Training
ICLR: Adversarial Policies: Attacking Deep Reinforcement Learning
ICLR: MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
ICLR: Enhancing Adversarial Defense by k-Winners-Take-All
ICLR: Intriguing Properties of Adversarial Training at Scale
ICLR: Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking
ICLR: Certified Defenses for Adversarial Patches
ICLR: Fast is better than free: Revisiting adversarial training
ICLR: Towards neural networks that provably know when they don't know
ICLR: GAT: Generative Adversarial Training for Adversarial Example Detection and Classification
ICLR: Improving Adversarial Robustness Requires Revisiting Misclassified Examples
ICLR: BayesOpt Adversarial Attack
ICLR: Robust anomaly detection and backdoor attack detection via differential privacy
ICLR: Adversarially Robust Representations with Smooth Encoders
ICLR: Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
ICLR: Unrestricted Adversarial Examples via Semantic Manipulation
ICLR: Defending Against Physically Realizable Attacks on Image Classification
ICLR: Black-Box Adversarial Attack with Transferable Model-based Embedding
ICLR: Robust Local Features for Improving the Generalization of Adversarial Training
ICLR: Optimal Strategies Against Generative Attacks
ICLR: Adversarially robust transfer learning
ICLR: Sign Bits Are All You Need for Black-Box Attacks
ICLR: Federated Adversarial Domain Adaptation
ICLR: Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
ICLR: Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
ICLR: Transferable Perturbations of Deep Feature Distributions
ICLR: Adversarial Training and Provable Defenses: Bridging the Gap
19
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks | OpenReview
Boosting Robustness Certification of Neural Networks | OpenReview
ADef: an Iterative Algorithm to Construct Adversarial Deformations | OpenReview
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations | OpenReview
On the Sensitivity of Adversarial Robustness to Input Data Distributions | OpenReview
Robustness May Be at Odds with Accuracy | OpenReview
Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network | OpenReview
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability | OpenReview
Don't let your Discriminator be fooled | OpenReview
Cost-Sensitive Robustness against Adversarial Examples | OpenReview
Are adversarial examples inevitable? | OpenReview
Prior Convictions: Black-box Adversarial Attacks with Bandits and Priors | OpenReview
Adversarial Attacks on Graph Neural Networks via Meta Learning | OpenReview
Adversarial Reprogramming of Neural Networks | OpenReview
Excessive Invariance Causes Adversarial Vulnerability | OpenReview
SPIGAN: Privileged Adversarial Learning from Simulation | OpenReview
Towards the first adversarially robust neural network model on MNIST | OpenReview
Improving the Generalization of Adversarial Training with Domain Adaptation | OpenReview
Generalizable Adversarial Training via Spectral Normalization | OpenReview
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures | OpenReview
The Limitations of Adversarial Training and the Blind-Spot Attack | OpenReview
Combinatorial Attacks on Binarized Neural Networks | OpenReview
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach | OpenReview
Characterizing Audio Adversarial Examples Using Temporal Dependency | OpenReview
18
Spatially Transformed Adversarial Examples | OpenReview
Towards Deep Learning Models Resistant to Adversarial Attacks | OpenReview
Decision Boundary Analysis of Adversarial Examples | OpenReview
Stochastic Activation Pruning for Robust Adversarial Defense | OpenReview
Cascade Adversarial Machine Learning Regularized with a Unified Embedding | OpenReview
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality | OpenReview
Thermometer Encoding: One Hot Way To Resist Adversarial Examples | OpenReview
Certified Defenses against Adversarial Examples | OpenReview
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models | OpenReview
Adversarial Dropout Regularization | OpenReview
Generating Natural Adversarial Examples | OpenReview
Countering Adversarial Images using Input Transformations | OpenReview
Ensemble Adversarial Training: Attacks and Defenses | OpenReview
Certifying Some Distributional Robustness with Principled Adversarial Training | OpenReview
Combating Adversarial Attacks Using Sparse Representations | OpenReview
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples | OpenReview
Black-box Attacks on Deep Neural Networks via Gradient Estimation | OpenReview
Intriguing Properties of Adversarial Examples | OpenReview
Adversarial Policy Gradient for Alternating Markov Games | OpenReview
17
[1611.01236] Adversarial Machine Learning at Scale
[1702.04267] On Detecting Adversarial Perturbations
[1611.02770] Delving into Transferable Adversarial Examples and Black-box Attacks
[1607.02533] Adversarial examples in the physical world
16
[1511.05122] Adversarial Manipulation of Deep Representations
15
[1412.6572] Explaining and Harnessing Adversarial Examples
[1412.5068] Towards Deep Neural Network Architectures Robust to Adversarial Examples
14
ChromeのブックマークをPythonで扱う
環境
目的
Bookmark のタイトルと URL を Markdown 形式で記述する.
PC 内に保存された JSON ファイルを用いる方法
import json path = 'C:\\Users\\{ユーザー名}\\AppData\\Local\\Google\\Chrome\\User Data\\Default\\Bookmarks' # windows with open(path, encoding='utf-8') as f: js_dict = json.load(f) bookmark_list = js_dict['roots']['bookmark_bar']['children'] for bookmark in bookmark_list: if bookmark['type'] == 'url': bookmark_name = bookmark['name'] url = bookmark['url'] print(f'[{bookmark_name}]({url})') print()
エキスポートされた HTML ファイルを使う方法
pip3 install beautifulsoup4
from bs4 import BeautifulSoup path = '/Users/kumano/Documents/bookmarks_2022_06_11.html' with open(path, encoding='utf-8') as f: contents = f.read() soup = BeautifulSoup(contents, features='html.parser') link_list = soup.find_all('a') for link in link_list: url = link.get('href') title = link.string print(f'[{title}]({url})') print()
結果
[Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem](https://openaccess.thecvf.com/content_CVPR_2019/papers/Hein_Why_ReLU_Networks_Yield_High-Confidence_Predictions_Far_Away_From_the_CVPR_2019_paper.pdf) [Feature Denoising for Improving Adversarial Robustness](https://openaccess.thecvf.com/content_CVPR_2019/papers/Xie_Feature_Denoising_for_Improving_Adversarial_Robustness_CVPR_2019_paper.pdf)
CVPR15~22のadversarial examples関連論文リンク集
目視で判断したので、間違っていたり抜けてたりするかもしれませんが、ご容赦ください。
22
Improving Adversarially Robust Few-shot Image Classification with Generalizable Representations
Bounded Adversarial Attack on Deep Content Features
[2203.01439] Enhancing Adversarial Robustness for Deep Metric Learning
[2112.05379] Cross-Modal Transferable Adversarial Attacks from Images to Videos
[2203.05151] Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity
[2009.00097] Adversarial Eigen Attack on Black-Box Models
[1910.00982] Adversarially Robust Few-Shot Learning: A Meta-Learning Approach
[2203.05154] Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack
[1809.02918] Towards Query Efficient Black-box Attacks: An Input-free Perspective
[2203.03373] Adversarial Texture for Fooling Person Detectors in the Physical World
[2204.02738] Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse Network
[2201.05057] On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles
[2203.06020] Enhancing Adversarial Training with Second-Order Statistics of Weights
[2111.12229] Subspace Adversarial Training
[2203.04041] Shape-invariant 3D Adversarial Point Clouds
[2111.15121] Pyramid Adversarial Training Improves ViT Performance
[2105.10123] Backdoor Attacks on Self-Supervised Learning
[2103.16255] Towards Understanding Adversarial Robustness of Optical Flow Networks
[2203.09566] Leveraging Adversarial Examples to Quantify Membership Information Leakage
[2111.12965] Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks
[2203.06616] LAS-AT: Adversarial Training with Learnable Attack Strategy
[2203.15674] Exploring Frequency Adversarial Attacks for Face Forgery Detection
[2011.06922] Image Animation with Perturbed Masks
[2105.14727] Transferable Sparse Adversarial Attack
[2012.12368] Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training
[2203.01925] Label-Only Model Inversion Attacks via Boundary Repulsion
21
Over-the-Air Adversarial Flickering Attacks Against Video Recognition Networks
Robust and Accurate Object Detection via Adversarial Learning
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect
VideoMoCo: Contrastive Video Representation Learning With Temporally Adversarial Examples
Delving into Data: Effectively Substitute Training for Black-box Attack
SurFree: A Fast Surrogate-Free Black-Box Attack
LiBRe: A Practical Bayesian Approach to Adversarial Detection
QAIR: Practical Query-Efficient Black-Box Attacks for Image Retrieval
Prototype-Supervised Adversarial Network for Targeted Attack of Deep Hashing
Regularizing Neural Networks via Adversarial Model Perturbation
Exploring Adversarial Fake Images on Face Manifold
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking
Improving Transferability of Adversarial Patches on Face Recognition With Generative Models
Architectural Adversarial Robustness: The Case for Deep Pursuit
Adversarial Robustness Under Long-Tailed Distribution
Achieving Robustness in Classification Using Optimal Transport With Hinge Regularization
Universal Spectral Adversarial Attacks for Deformable Shapes
Simulating Unknown Target Models for Query-Efficient Black-Box Attacks
Class-Aware Robust Adversarial Training for Object Detection
Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World
Improving the Transferability of Adversarial Samples With Adversarial Transformations
LAFEAT: Piercing Through Adversarial Defenses With Latent Features
How Robust Are Randomized Smoothing Based Defenses to Data Poisoning?
Understanding the Robustness of Skeleton-Based Action Recognition Under Adversarial Attack
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation
Explaining Classifiers Using Adversarial Perturbations on the Perceptual Ball
Can Audio-Visual Integration Strengthen Robustness Under Multimodal Attacks?
Adversarial Robustness Across Representation Spaces
20
Towards Transferable Targeted Attack
Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance
ColorFool: Semantic Adversarial Colorization
Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations
Boosting the Transferability of Adversarial Samples via Attention
Learn2Perturb: An End-to-End Feature Perturbation Learning to Improve Adversarial Robustness
On Isometry Robustness of Deep 3D Point Cloud Models Under Adversarial Attacks
Adversarial Examples Improve Image Recognition
Efficient Adversarial Training With Transferable Adversarial Examples
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Adversarial Texture Optimization From RGB-D Scans
Modeling Biological Immunity to Adversarial Examples
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction
Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles
Benchmarking Adversarial Robustness on Image Classification
What It Thinks Is Important Is Important: Robustness Transfers Through Input Gradients
How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework
A Self-supervised Approach for Adversarial Robustness
Ensemble Generative Cleaning With Feedback Loops for Defending Adversarial Attacks
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Projection & Probability-Driven Black-Box Attack
When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks
QEBA: Query-Efficient Boundary-Based Blackbox Attack
Universal Physical Camouflage Attacks on Object Detectors
Defending Against Universal Attacks Through Selective Feature Regeneration
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder
Robust Design of Deep Neural Networks Against Adversarial Attacks Based on Lyapunov Theory
Progressive Adversarial Networks for Fine-Grained Domain Adaptation
AdversarialNAS: Adversarial Neural Architecture Search for GANs
Attack to Explain Deep Representation
Towards Universal Representation Learning for Deep Face Recognition
Non-Adversarial Video Synthesis With Learned Priors
Physically Realizable Adversarial Examples for LiDAR Object Detection
Defending and Harnessing the Bit-Flip Based Adversarial Weight Attack
Adversarial Feature Hallucination Networks for Few-Shot Learning
Robust Superpixel-Guided Attentional Adversarial Attack
ILFO: Adversarial Attack on Adaptive Neural Networks
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
M2m: Imbalanced Classification via Major-to-minor Translation
19
Feature Denoising for Improving Adversarial Robustness
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Improving Transferability of Adversarial Examples With Input Diversity
Exact Adversarial Attack to Image Captioning via Structured Output Learning With Latent Variables
Adversarial Attacks Beyond the Image Space
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks?
Handwriting Recognition in Low-Resource Scripts Using Adversarial Learning
Adversarial Defense Through Network Profiling Based Path Extraction
Detection Based Defense Against Adversarial Examples From the Steganalysis Point of View
Curls & Whey: Boosting Black-Box Adversarial Attacks
Barrage of Random Transforms for Adversarially Robust Defense
Adversarial Inference for Multi-Sentence Video Description
MeshAdv: Adversarial Meshes for Visual Recognition
Disentangling Adversarial Robustness and Generalization
ShieldNets: Defending Against Adversarial Attacks Using Probabilistic Adversarial Robustness
Feature Space Perturbations Yield More Transferable Adversarial Examples
Defense Against Adversarial Images Using Web-Scale Nearest-Neighbor Search
SparseFool: A Few Pixels Make a Big Difference
Generating 3D Adversarial Point Clouds
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks
Defending Against Adversarial Attacks by Randomized Diversification
Rob-GAN: Generator, Discriminator, and Adversarial Attacker
Trust Region Based Adversarial Attack on Neural Networks
Adversarial Defense by Stratified Convolutional Sparse Coding
Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples
Robustness of 3D Deep Learning in an Adversarial Setting
[1904.08653] Fooling automated surveillance cameras: adversarial patches to attack person detection
18
On the Robustness of Semantic Segmentation Models to Adversarial Attacks
NAG: Network for Adversary Generation
Robust Physical-World Attacks on Deep Learning Visual Classification
Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser
Adversarially Learned One-Class Classifier for Novelty Detection
Defense Against Universal Adversarial Perturbations
Generative Adversarial Perturbations
Adversarially Occluded Samples for Person Re-Identification
Boosting Adversarial Attacks With Momentum
17
A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
Universal Adversarial Perturbations
16
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
15
Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images
