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 576–600 of 1808 papers

TitleStatusHype
Emotion Loss Attacking: Adversarial Attack Perception for Skeleton based on Multi-dimensional Features—0
IDT: Dual-Task Adversarial Attacks for Privacy Protection—0
Deceptive Diffusion: Generating Synthetic Adversarial Examples—0
CuDA2: An approach for Incorporating Traitor Agents into Cooperative Multi-Agent Systems—0
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification—0
The Effect of Similarity Measures on Accurate Stability Estimates for Local Surrogate Models in Text-based Explainable AI—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization—0
Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation—0
Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor AttacksCode0
Imperceptible Face Forgery Attack via Adversarial Semantic MaskCode0
KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge GraphsCode0
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
VQUNet: Vector Quantization U-Net for Defending Adversarial Atacks by Regularizing Unwanted Noise—0
Graph Neural Network Explanations are FragileCode0
SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural NetworksCode0
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
Rethinking Independent Cross-Entropy Loss For Graph-Structured DataCode0
Adversarial Attacks on Hidden Tasks in Multi-Task Learning—0
AdjointDEIS: Efficient Gradients for Diffusion ModelsCode0
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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