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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1426–1450 of 1808 papers

TitleStatusHype
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