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 1626–1650 of 1808 papers

TitleStatusHype
Darknet Traffic Classification and Adversarial Attacks—0
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet—0
Universal Adversarial Attack on Deep Learning Based Prognostics—0
D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack—0
DDR-ID: Dual Deep Reconstruction Networks Based Image Decomposition for Anomaly Detection—0
Universal Adversarial Attack Using Very Few Test Examples—0
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias—0
Deceptive Diffusion: Generating Synthetic Adversarial Examples—0
Adversarial Attacks against Deep Saliency Models—0
A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks—0
Decoder Gradient Shield: Provable and High-Fidelity Prevention of Gradient-Based Box-Free Watermark Removal—0
Similarity of Neural Architectures using Adversarial Attack Transferability—0
Simple and Effective Stochastic Neural Networks—0
Deep adversarial attack on target detection systems—0
Deep-Attack over the Deep Reinforcement Learning—0
Universal Adversarial Perturbations and Image Spam Classifiers—0
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System—0
Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis—0
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems—0
Adversarial Attacks Against Deep Learning Systems for ICD-9 Code Assignment—0
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation—0
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment—0
Deep Learning for Robust and Explainable Models in Computer Vision—0
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs—0
Deep-RBF Networks Revisited: Robust Classification with Rejection—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