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 76100 of 1808 papers

TitleStatusHype
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language ModelsCode1
Targeted Attack Improves Protection against Unauthorized Diffusion CustomizationCode1
Robustness of AI-Image Detectors: Fundamental Limits and Practical AttacksCode1
Structure Invariant Transformation for better Adversarial TransferabilityCode1
Semantic Adversarial Attacks via Diffusion ModelsCode1
RAIN: Your Language Models Can Align Themselves without FinetuningCode1
Differentiable JPEG: The Devil is in the DetailsCode1
Certifying LLM Safety against Adversarial PromptingCode1
PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model ModificationCode1
On the Adversarial Robustness of Multi-Modal Foundation ModelsCode1
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed GradientCode1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
Multi-attacks: Many images + the same adversarial attack many target labelsCode1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
OUTFOX: LLM-Generated Essay Detection Through In-Context Learning with Adversarially Generated ExamplesCode1
Frequency Domain Adversarial Training for Robust Volumetric Medical SegmentationCode1
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial AttacksCode1
White-Box Multi-Objective Adversarial Attack on Dialogue GenerationCode1
Fooling the Image Dehazing Models by First Order GradientCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Feature Separation and Recalibration for Adversarial RobustnessCode1
X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item DetectionCode1
StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot LearningCode1
Robust Mid-Pass Filtering Graph Convolutional NetworksCode1
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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