SOTAVerified

Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

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Showing 1401–1450 of 1808 papers

TitleStatusHype
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Query-Free Adversarial Transfer via Undertrained Surrogates—0
Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial AttacksCode0
Generating Adversarial Examples with an Optimized Quality—0
RayS: A Ray Searching Method for Hard-label Adversarial AttackCode1
Adversarial Attacks for Multi-view Deep Models—0
Differentiable Language Model Adversarial Attacks on Categorical Sequence ClassifiersCode1
Local Competition and Uncertainty for Adversarial Robustness in Deep Learning—0
REGroup: Rank-aggregating Ensemble of Generative Classifiers for Robust PredictionsCode0
OGAN: Disrupting Deepfakes with an Adversarial Attack that Survives Training—0
Classifier-independent Lower-Bounds for Adversarial Robustness—0
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial DistributionCode1
Adversarial Self-Supervised Contrastive LearningCode1
Targeted Adversarial Perturbations for Monocular Depth PredictionCode1
D-square-B: Deep Distribution Bound for Natural-looking Adversarial Attack—0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples—0
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored FactorsCode0
Interpolation between Residual and Non-Residual NetworksCode1
Global Robustness Verification Networks—0
Pick-Object-Attack: Type-Specific Adversarial Attack for Object DetectionCode1
One-Shot Adversarial Attacks on Visual Tracking With Dual Attention—0
Robust Superpixel-Guided Attentional Adversarial Attack—0
What Machines See Is Not What They Get: Fooling Scene Text Recognition Models With Adversarial Text Images—0
Modeling Biological Immunity to Adversarial Examples—0
Benchmarking Adversarial Robustness on Image ClassificationCode1
Defending and Harnessing the Bit-Flip Based Adversarial Weight AttackCode1
Polishing Decision-Based Adversarial Noise With a Customized Sampling—0
ILFO: Adversarial Attack on Adaptive Neural Networks—0
Evaluations and Methods for Explanation through Robustness Analysis—0
Effects of Forward Error Correction on Communications Aware Evasion Attacks—0
Generating Semantically Valid Adversarial Questions for TableQA—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning—0
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack—0
An Adversarial Approach for Explaining the Predictions of Deep Neural NetworksCode0
On Intrinsic Dataset Properties for Adversarial Machine LearningCode1
Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspectiveCode1
Universalization of any adversarial attack using very few test examplesCode0
Defending Your Voice: Adversarial Attack on Voice ConversionCode1
Attacking Recommender Systems with Augmented User ProfilesCode1
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning—0
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers—0
Class-Aware Domain Adaptation for Improving Adversarial Robustness—0
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack—0
Depth-2 Neural Networks Under a Data-Poisoning AttackCode0
BayesOpt Adversarial AttackCode1
Enhancing Transformation-Based Defenses Against Adversarial Attacks with a Distribution Classifier—0
Sign Bits Are All You Need for Black-Box AttacksCode1
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPCode2
Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability—0
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All datasetsCIFAR-10CIFAR-100

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68—Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13—Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7—Unverified
4AdvTraining [madry2018]Attack: PGD2048.44—Unverified
5TRADES [zhang2019b]Attack: PGD2045.9—Unverified
6XU-NetRobust Accuracy1—Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28—Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85—Unverified