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

Showing 201225 of 1808 papers

TitleStatusHype
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
Meta Gradient Adversarial AttackCode1
Poison Ink: Robust and Invisible Backdoor AttackCode1
Discriminator-Free Generative Adversarial AttackCode1
Adversarial Attack on Graph Neural Networks as An Influence Maximization ProblemCode1
TDGIA:Effective Injection Attacks on Graph Neural NetworksCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
On Improving Adversarial Transferability of Vision TransformersCode1
Adversarial Attack and Defense in Deep RankingCode1
Transferable Sparse Adversarial AttackCode1
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating DeepfakesCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
Improving Adversarial Transferability with Gradient RefiningCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
3D Adversarial Attacks Beyond Point CloudCode1
Staircase Sign Method for Boosting Adversarial AttacksCode1
R&R: Metric-guided Adversarial Sentence GenerationCode1
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object TrackingCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness VerificationCode1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier DomainCode1
A Survey On Universal Adversarial AttackCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Xu et al.Attack: PGD2078.68Unverified
23-ensemble of multi-resolution self-ensemblesAttack: AutoAttack78.13Unverified
3TRADES-ANCRA/ResNet18Attack: AutoAttack59.7Unverified
4AdvTraining [madry2018]Attack: PGD2048.44Unverified
5TRADES [zhang2019b]Attack: PGD2045.9Unverified
6XU-NetRobust Accuracy1Unverified
#ModelMetricClaimedVerifiedStatus
13-ensemble of multi-resolution self-ensemblesAttack: AutoAttack51.28Unverified
2multi-resolution self-ensemblesAttack: AutoAttack47.85Unverified