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 151–200 of 1808 papers

TitleStatusHype
Composite Adversarial AttacksCode1
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat LandscapeCode1
Adversarial Attack and Defense in Deep RankingCode1
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency DomainCode1
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular DataCode1
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial TransferabilityCode1
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
Efficient Training of Robust Decision Trees Against Adversarial ExamplesCode1
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality MetricsCode1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake DetectionCode1
Adversarial Ranking Attack and DefenseCode1
Adversarial Training with Fast Gradient Projection Method against Synonym Substitution based Text AttacksCode1
AdvDrop: Adversarial Attack to DNNs by Dropping InformationCode1
An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacksCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Feature Separation and Recalibration for Adversarial RobustnessCode1
Adversarial Examples for Semantic Segmentation and Object DetectionCode1
3D Gaussian Splat VulnerabilitiesCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
A Pilot Study of Query-Free Adversarial Attack against Stable DiffusionCode1
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV TrackingCode1
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and BeyondCode1
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language ModelsCode1
Are AlphaZero-like Agents Robust to Adversarial Perturbations?Code1
A Survey On Universal Adversarial AttackCode1
R&R: Metric-guided Adversarial Sentence GenerationCode1
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative modelCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Attacking Recommender Systems with Augmented User ProfilesCode1
Generalizing Universal Adversarial Attacks Beyond Additive PerturbationsCode1
CausalAdv: Adversarial Robustness through the Lens of CausalityCode1
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a BlinkCode1
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query AttacksCode1
Adversarial Learning for Robust Deep ClusteringCode1
Adversarial Attack on Community Detection by Hiding IndividualsCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
Audio Jailbreak Attacks: Exposing Vulnerabilities in SpeechGPT in a White-Box FrameworkCode1
Hard No-Box Adversarial Attack on Skeleton-Based Human Action Recognition with Skeleton-Motion-Informed GradientCode1
Adversarial Attack on Deep Learning-Based Splice LocalizationCode1
Augmented Lagrangian Adversarial AttacksCode1
Data-free Universal Adversarial Perturbation with Pseudo-semantic PriorCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Contextualized Perturbation for Textual Adversarial AttackCode1
On the Multi-modal Vulnerability of Diffusion ModelsCode1
Adversarial Attack on Graph Neural Networks as An Influence Maximization ProblemCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
High Frequency Component Helps Explain the Generalization of Convolutional Neural NetworksCode1
Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition ModelCode1
Benchmarking Adversarial Robustness on Image ClassificationCode1
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating DeepfakesCode1
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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