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 851–875 of 1808 papers

TitleStatusHype
Generating Valid and Natural Adversarial Examples with Large Language Models—0
Generating Watermarked Adversarial Texts—0
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs—0
Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification—0
Defending Against Adversarial Examples by Regularized Deep Embedding—0
Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training—0
Global Robustness Verification Networks—0
Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification—0
ImF: Implicit Fingerprint for Large Language Models—0
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
Improved Adversarial Training via Learned Optimizer—0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training—0
Beyond Dropout: Robust Convolutional Neural Networks Based on Local Feature Masking—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
A Differentiable Language Model Adversarial Attack on Text Classifiers—0
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents—0
Beyond Score Changes: Adversarial Attack on No-Reference Image Quality Assessment from Two Perspectives—0
Deep-RBF Networks Revisited: Robust Classification with Rejection—0
GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm—0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs—0
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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