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 326–350 of 1808 papers

TitleStatusHype
Adversarial Attacks Neutralization via Data Set Randomization—0
AdvCodeMix: Adversarial Attack on Code-Mixed Data—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
Adversarial Attacks in Sound Event Classification—0
AdvSmo: Black-box Adversarial Attack by Smoothing Linear Structure of Texture—0
Patch Synthesis for Property Repair of Deep Neural Networks—0
Adversarial Attacks in Multimodal Systems: A Practitioner's Survey—0
Adversarial Attack for Asynchronous Event-based Data—0
AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systems—0
Adversarial Attacks for Optical Flow-Based Action Recognition Classifiers—0
Natural & Adversarial Bokeh Rendering via Circle-of-Confusion Predictive Network—0
AdvMask: A Sparse Adversarial Attack Based Data Augmentation Method for Image Classification—0
Adversarial Attacks for Multi-view Deep Models—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
AdvHaze: Adversarial Haze Attack—0
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems—0
Unsourced Adversarial CAPTCHA: A Bi-Phase Adversarial CAPTCHA Framework—0
Adversarial Attacks and Dimensionality in Text Classifiers—0
AdvGen: Physical Adversarial Attack on Face Presentation Attack Detection Systems—0
Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition—0
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off—0
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning—0
Adverseness vs. Equilibrium: Exploring Graph Adversarial Resilience through Dynamic Equilibrium—0
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks—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