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 301–325 of 1808 papers

TitleStatusHype
Imperceptible Face Forgery Attack via Adversarial Semantic MaskCode0
Explainable Graph Neural Networks Under FireCode0
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks—0
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner—0
Graph Neural Network Explanations are FragileCode0
DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-DomainCode1
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise—0
SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural NetworksCode0
Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular DataCode1
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based CustomizationCode1
Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function PriorCode0
Wavelet-Based Image Tokenizer for Vision Transformers—0
Uncertainty Measurement of Deep Learning System based on the Convex Hull of Training Sets—0
Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack—0
Adversarial Attacks on Hidden Tasks in Multi-Task Learning—0
Rethinking Independent Cross-Entropy Loss For Graph-Structured DataCode0
AdjointDEIS: Efficient Gradients for Diffusion ModelsCode0
LookHere: Vision Transformers with Directed Attention Generalize and ExtrapolateCode0
Trustworthy Actionable Perturbations—0
Safeguarding Vision-Language Models Against Patched Visual Prompt Injectors—0
Adversarial Robustness for Visual Grounding of Multimodal Large Language ModelsCode0
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy ProtectionCode2
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer—0
Improving Transferable Targeted Adversarial Attack via Normalized Logit Calibration and Truncated Feature Mixing—0
Disttack: Graph Adversarial Attacks Toward Distributed GNN TrainingCode0
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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