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

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Showing 201–250 of 1808 papers

TitleStatusHype
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial TrainingCode1
Adversarial Attack on Large Scale GraphCode1
Learning Safety Constraints for Large Language ModelsCode1
Local Gradients Smoothing: Defense against localized adversarial attacksCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
Meta Gradient Adversarial AttackCode1
Adversarial Ranking Attack and DefenseCode1
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style TransferCode1
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
Motion-Excited Sampler: Video Adversarial Attack with Sparked PriorCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Multi-granularity Textual Adversarial Attack with Behavior CloningCode1
High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
An Orthogonal Classifier for Improving the Adversarial Robustness of Neural NetworksCode1
On Adversarial Robustness of Trajectory Prediction for Autonomous VehiclesCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
On Evaluating Adversarial RobustnessCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
On Improving Adversarial Transferability of Vision TransformersCode1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat LandscapeCode1
Adversarial Self-Supervised Contrastive LearningCode1
Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking ModelsCode1
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated ExamplesCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
Adversarial Training for Free!Code1
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery DetectionCode1
Perturbation Inactivation Based Adversarial Defense for Face RecognitionCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Physical Adversarial Attack meets Computer Vision: A Decade SurveyCode1
Attacking Recommender Systems with Augmented User ProfilesCode1
An Efficient Adversarial Attack for Tree EnsemblesCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
An Extensive Study on Adversarial Attack against Pre-trained Models of CodeCode1
Proximal Splitting Adversarial Attacks for Semantic SegmentationCode1
Random Walks for Adversarial MeshesCode1
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?Code1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural NetworksCode1
Rethinking Image Restoration for Object DetectionCode1
Revealing Vulnerabilities in Stable Diffusion via Targeted AttacksCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Robust Mid-Pass Filtering Graph Convolutional NetworksCode1
Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android AppsCode1
Benchmarking Adversarial Robustness on Image ClassificationCode1
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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