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 51–75 of 1808 papers

TitleStatusHype
EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability DetectionCode1
PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous DrivingCode1
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation ModelsCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-DomainCode1
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular DataCode1
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based CustomizationCode1
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation ModelsCode1
Universal Adversarial Perturbations for Vision-Language Pre-trained ModelsCode1
Revisiting Character-level Adversarial Attacks for Language ModelsCode1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat LandscapeCode1
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression RecognitionCode1
Hide in Thicket: Generating Imperceptible and Rational Adversarial Perturbations on 3D Point CloudsCode1
RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage GenerationCode1
On the Multi-modal Vulnerability of Diffusion ModelsCode1
Benchmarking Transferable Adversarial AttacksCode1
Fluent dreaming for language modelsCode1
Revealing Vulnerabilities in Stable Diffusion via Targeted AttacksCode1
The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical ImagesCode1
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative modelCode1
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision TransformersCode1
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity TrainingCode1
Towards Transferable Targeted 3D Adversarial Attack in the Physical WorldCode1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake DetectionCode1
An Extensive Study on Adversarial Attack against Pre-trained Models of CodeCode1
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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