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 1376–1400 of 1808 papers

TitleStatusHype
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs—0
Adversarial Robustness for Machine Learning Cyber Defenses Using Log Data—0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning—0
Attacking and Defending Machine Learning Applications of Public CloudCode2
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing—0
From Sound Representation to Model Robustness—0
Adversarial Privacy-preserving FilterCode0
T-BFA: Targeted Bit-Flip Adversarial Weight AttackCode0
Robust Tracking against Adversarial AttacksCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Semantic Equivalent Adversarial Data Augmentation for Visual Question AnsweringCode1
Exploiting vulnerabilities of deep neural networks for privacy protectionCode0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection—0
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense—0
Backdoor Learning: A SurveyCode2
Accelerated Stochastic Gradient-free and Projection-free MethodsCode0
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
Patch-wise Attack for Fooling Deep Neural NetworkCode1
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack—0
Generating Adversarial Inputs Using A Black-box Differential Technique—0
Miss the Point: Targeted Adversarial Attack on Multiple Landmark DetectionCode1
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
Black-box Adversarial Example Generation with Normalizing FlowsCode1
On Data Augmentation and Adversarial Risk: An Empirical Analysis—0
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain—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