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 1101–1125 of 1808 papers

TitleStatusHype
The FEVER2.0 Shared Task—0
The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models—0
There are No Bit Parts for Sign Bits in Black-Box Attacks—0
The Relationship Between Network Similarity and Transferability of Adversarial Attacks—0
Thundernna: a white box adversarial attack—0
Thwarting finite difference adversarial attacks with output randomization—0
Time-aware Gradient Attack on Dynamic Network Link Prediction—0
To be Robust and to be Fair: Aligning Fairness with Robustness—0
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models—0
BOSH: An Efficient Meta Algorithm for Decision-based Attacks—0
Towards Accuracy-Fairness Paradox: Adversarial Example-based Data Augmentation for Visual Debiasing—0
Towards Adversarially Robust Deep Image Denoising—0
Towards a Novel Perspective on Adversarial Examples Driven by Frequency—0
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness—0
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios—0
Towards Building a Robust Toxicity Predictor—0
Towards Calibration Enhanced Network by Inverse Adversarial Attack—0
Towards Certified Defense for Unrestricted Adversarial Attacks—0
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty—0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer—0
Fooling Network Interpretation in Image Classification—0
Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning—0
Towards Leveraging the Information of Gradients in Optimization-based Adversarial Attack—0
Towards more transferable adversarial attack in black-box manner—0
Towards Natural Robustness Against Adversarial Examples—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