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 826–850 of 1808 papers

TitleStatusHype
AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture—0
SSMI: How to Make Objects of Interest Disappear without Accessing Object Detectors?—0
Towards Adversarial Attack on Vision-Language Pre-training ModelsCode1
Detecting Adversarial Examples in Batches -- a geometrical approachCode0
Boosting the Adversarial Transferability of Surrogate Models with Dark KnowledgeCode1
Proximal Splitting Adversarial Attacks for Semantic SegmentationCode1
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective—0
Adversarial Vulnerability of Randomized EnsemblesCode1
Darknet Traffic Classification and Adversarial Attacks—0
AS2T: Arbitrary Source-To-Target Adversarial Attack on Speaker Recognition Systems—0
Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning—0
Saliency Attack: Towards Imperceptible Black-box Adversarial AttackCode0
Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline—0
Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNsCode0
Attack-Agnostic Adversarial Detection—0
On the Perils of Cascading Robust ClassifiersCode0
On the reversibility of adversarial attacks—0
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural NetworksCode1
Semantic Autoencoder and Its Potential Usage for Adversarial Attack—0
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models—0
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks—0
On the Robustness of Safe Reinforcement Learning under Observational PerturbationsCode1
Superclass Adversarial Attack—0
Unfooling Perturbation-Based Post Hoc ExplainersCode0
Physical-World Optical Adversarial Attacks on 3D Face Recognition—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