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 1451–1500 of 1808 papers

TitleStatusHype
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?Code0
Another Dead End for Morphological Tags? Perturbed Inputs and ParsingCode0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Evaluating and Understanding the Robustness of Adversarial Logit PairingCode0
Geometry-Aware Generation of Adversarial Point CloudsCode0
Semantic-Aware Adversarial Training for Reliable Deep Hashing RetrievalCode0
CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the WildCode0
Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural NetworksCode0
Angelic Patches for Improving Third-Party Object Detector PerformanceCode0
CAAD 2018: Generating Transferable Adversarial ExamplesCode0
TransFool: An Adversarial Attack against Neural Machine Translation ModelsCode0
Translate your gibberish: black-box adversarial attack on machine translation systemsCode0
Graph Adversarial Immunization for Certifiable RobustnessCode0
Graph-based methods coupled with specific distributional distances for adversarial attack detectionCode0
Adversarial Attack on Large Language Models using Exponentiated Gradient DescentCode0
Physics-constrained Attack against Convolution-based Human Motion PredictionCode0
Adversarial Attack on Graph Structured DataCode0
Graph Neural Network Explanations are FragileCode0
Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language ModelsCode0
GreedyFool: Multi-Factor Imperceptibility and Its Application to Designing a Black-box Adversarial AttackCode0
EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware DetectionCode0
PointACL:Adversarial Contrastive Learning for Robust Point Clouds Representation under Adversarial AttackCode0
Grey-box Adversarial Attack And Defence For Sentiment ClassificationCode0
Depth-2 Neural Networks Under a Data-Poisoning AttackCode0
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust AccuraciesCode0
Hard-label based Small Query Black-box Adversarial AttackCode0
Improving Sequence Modeling Ability of Recurrent Neural Networks via SememesCode0
Unpacking the Resilience of SNLI Contradiction Examples to AttacksCode0
A New Ensemble Adversarial Attack Powered by Long-term Gradient MemoriesCode0
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object DetectorCode0
Harnessing the Vulnerability of Latent Layers in Adversarially Trained ModelsCode0
Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target ScenesCode0
Bridging the Performance Gap between FGSM and PGD Adversarial TrainingCode0
Towards Adaptive Meta-Gradient Adversarial Examples for Visual TrackingCode0
Adversarial Examples in Modern Machine Learning: A ReviewCode0
An Empirical Investigation of Randomized Defenses against Adversarial AttacksCode0
Trust Region Based Adversarial Attack on Neural NetworksCode0
Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face RecognitionCode0
Heuristic Black-box Adversarial Attacks on Video Recognition ModelsCode0
Towards Adversarial Patch Analysis and Certified Defense against Crowd CountingCode0
Enhancing Neural Models with Vulnerability via Adversarial AttackCode0
Towards adversarial robustness verification of no-reference image-and video-quality metricsCode0
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion ReductionCode0
Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy ProtectionCode0
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image SynthesisCode0
Practical Relative Order Attack in Deep RankingCode0
An adversarial attack approach for eXplainable AI evaluation on deepfake detection modelsCode0
Sign-OPT: A Query-Efficient Hard-label Adversarial AttackCode0
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model ReliabilityCode0
How Private Is Your RL Policy? An Inverse RL Based Analysis FrameworkCode0
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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