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
MORA: Improving Ensemble Robustness Evaluation with Model-Reweighing AttackCode1
Motion-Excited Sampler: Video Adversarial Attack with Sparked PriorCode1
Adversarial Attack on Large Scale GraphCode1
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation ModelsCode1
Natural Color Fool: Towards Boosting Black-box Unrestricted AttacksCode1
Nesterov Accelerated Gradient and Scale Invariance for Adversarial AttacksCode1
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation ModelsCode1
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based RetrievalCode1
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric LearningCode1
CARBEN: Composite Adversarial Robustness BenchmarkCode1
Adversarial Attack On Yolov5 For Traffic And Road Sign DetectionCode1
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords SubstitutionCode1
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNsCode1
Certifying LLM Safety against Adversarial PromptingCode1
High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksCode1
On the Multi-modal Vulnerability of Diffusion ModelsCode1
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating DeepfakesCode1
On the Adversarial Robustness of Camera-based 3D Object DetectionCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
OpenAttack: An Open-source Textual Adversarial Attack ToolkitCode1
Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-ArtCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Cooling-Shrinking Attack: Blinding the Tracker with Imperceptible NoisesCode1
Adversarial Self-Supervised Contrastive LearningCode1
PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification ModelsCode1
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation ModelsCode1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
Adversarial Training for Free!Code1
AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion ModelsCode1
Physical Adversarial Attack meets Computer Vision: A Decade SurveyCode1
Adversarial Vulnerabilities in Large Language Models for Time Series ForecastingCode1
Adversarial Vulnerability of Randomized EnsemblesCode1
DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-DomainCode1
Preserving Semantics in Textual Adversarial AttacksCode1
Differentiable JPEG: The Devil is in the DetailsCode1
Proximal Splitting Adversarial Attack for Semantic SegmentationCode1
Adversarial Attacks and Detection in Visual Place Recognition for Safer Robot NavigationCode1
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing FlowsCode1
RayS: A Ray Searching Method for Hard-label Adversarial AttackCode1
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural NetworksCode1
Deep Feature Space Trojan Attack of Neural Networks by Controlled DetoxificationCode1
Rethinking Image Restoration for Object DetectionCode1
Adv-Makeup: A New Imperceptible and Transferable Attack on Face RecognitionCode1
Deep Variational Information BottleneckCode1
Defending and Harnessing the Bit-Flip Based Adversarial Weight AttackCode1
Robust Deep Reinforcement Learning through Adversarial LossCode1
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted AttackCode1
Alleviating Adversarial Attacks on Variational Autoencoders with MCMCCode1
Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless NetworksCode1
Disentangled Information BottleneckCode1
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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