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 1001–1025 of 1808 papers

TitleStatusHype
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models—0
Jitter Does Matter: Adapting Gaze Estimation to New Domains—0
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack—0
Keep on Swimming: Real Attackers Only Need Partial Knowledge of a Multi-Model System—0
Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System—0
AdvCodeMix: Adversarial Attack on Code-Mixed Data—0
Zero-Query Transfer Attacks on Context-Aware Object Detectors—0
KoDF: A Large-scale Korean DeepFake Detection Dataset—0
Label Smoothing and Adversarial Robustness—0
LanCe: A Comprehensive and Lightweight CNN Defense Methodology against Physical Adversarial Attacks on Embedded Multimedia Applications—0
Large-Scale Adversarial Attacks on Graph Neural Networks via Graph Coarsening—0
Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Space—0
AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation—0
AdvSwap: Covert Adversarial Perturbation with High Frequency Info-swapping for Autonomous Driving Perception—0
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions—0
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks—0
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations—0
There are No Bit Parts for Sign Bits in Black-Box Attacks—0
The Relationship Between Network Similarity and Transferability of Adversarial Attacks—0
Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing—0
Learning Globally Optimized Language Structure via Adversarial Training—0
Learning Key Steps to Attack Deep Reinforcement Learning Agents—0
AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture—0
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning—0
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations—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