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 1001–1050 of 1808 papers

TitleStatusHype
Defending Substitution-Based Profile Pollution Attacks on Sequential RecommendersCode0
Multi-step domain adaptation by adversarial attack to H ΔH-divergence—0
DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking—0
Adversarial Examples for Model-Based Control: A Sensitivity Analysis—0
How many perturbations break this model? Evaluating robustness beyond adversarial accuracyCode0
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network—0
Query-Efficient Adversarial Attack Based on Latin Hypercube SamplingCode0
Learning to Accelerate Approximate Methods for Solving Integer Programming via Early FixingCode0
RAF: Recursive Adversarial Attacks on Face Recognition Using Extremely Limited Queries—0
ZhichunRoad at SemEval-2022 Task 2: Adversarial Training and Contrastive Learning for Multiword Representations—0
Resilience of Named Entity Recognition Models under Adversarial AttackCode0
SHARP: Search-Based Adversarial Attack for Structured Prediction—0
Robustness of Explanation Methods for NLP Models—0
A Framework for Understanding Model Extraction Attack and Defense—0
Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs—0
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
Detecting Adversarial Examples in Batches -- a geometrical approachCode0
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective—0
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
On the reversibility of adversarial attacks—0
On the Perils of Cascading Robust ClassifiersCode0
Attack-Agnostic Adversarial Detection—0
Semantic Autoencoder and Its Potential Usage for Adversarial Attack—0
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models—0
Unfooling Perturbation-Based Post Hoc ExplainersCode0
Superclass Adversarial Attack—0
Mixture GAN For Modulation Classification Resiliency Against Adversarial Attacks—0
Physical-World Optical Adversarial Attacks on 3D Face Recognition—0
Adversarial Body Shape Search for Legged Robots—0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Transferable Physical Attack against Object Detection with Separable Attention—0
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds—0
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks—0
Btech thesis report on adversarial attack detection and purification of adverserially attacked images—0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems—0
CE-based white-box adversarial attacks will not work using super-fitting—0
Rethinking Classifier and Adversarial Attack—0
Deep-Attack over the Deep Reinforcement Learning—0
BERTops: Studying BERT Representations under a Topological LensCode0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
Adversarial attacks on an optical neural network—0
Adversarial Fine-tune with Dynamically Regulated Adversary—0
An Adversarial Attack Analysis on Malicious Advertisement URL Detection FrameworkCode0
Mixed Strategies for Security Games with General Defending Requirements—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