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 1176–1200 of 1808 papers

TitleStatusHype
Challenging the adversarial robustness of DNNs based on error-correcting output codes—0
Adversarially Robust Neural Architectures—0
Towards Natural Robustness Against Adversarial Examples—0
On the Effectiveness of Low Frequency Perturbations—0
On the existence of consistent adversarial attacks in high-dimensional linear classification—0
On the feasibility of attacking Thai LPR systems with adversarial examples—0
On the Optimal Interaction Range for Multi-Agent Systems Under Adversarial Attack—0
Toward Spiking Neural Network Local Learning Modules Resistant to Adversarial Attacks—0
Towards Security Threats of Deep Learning Systems: A Survey—0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network—0
On the reversibility of adversarial attacks—0
On the Robustness of Domain Adaption to Adversarial Attacks—0
Adversarially robust generalization theory via Jacobian regularization for deep neural networks—0
On the Robustness of Split Learning against Adversarial Attacks—0
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks—0
On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense—0
On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples—0
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix—0
OOWL500: Overcoming Dataset Collection Bias in the Wild—0
Adversarially robust deepfake media detection using fused convolutional neural network predictions—0
OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation—0
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks—0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations—0
Optical Adversarial Attack—0
Optimal Attack against Autoregressive Models by Manipulating the Environment—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