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 1051–1100 of 1808 papers

TitleStatusHype
Restricted Black-box Adversarial Attack Against DeepFake Face Swapping—0
Boosting Adversarial Transferability of MLP-Mixer—0
Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity—0
How Sampling Impacts the Robustness of Stochastic Neural Networks—0
Enhancing the Transferability via Feature-Momentum Adversarial Attack—0
A Mask-Based Adversarial Defense Scheme—0
Testing robustness of predictions of trained classifiers against naturally occurring perturbations—0
Metamorphic Testing-based Adversarial Attack to Fool Deepfake Detectors—0
UNBUS: Uncertainty-aware Deep Botnet Detection System in Presence of Perturbed Samples—0
Residue-Based Natural Language Adversarial Attack DetectionCode0
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case—0
From Environmental Sound Representation to Robustness of 2D CNN Models Against Adversarial Attacks—0
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition—0
Adversarial Neon Beam: A Light-based Physical Attack to DNNs—0
Zero-Query Transfer Attacks on Context-Aware Object Detectors—0
Exploring Frequency Adversarial Attacks for Face Forgery Detection—0
Boosting Black-Box Adversarial Attacks with Meta Learning—0
Text Adversarial Purification as Defense against Adversarial Attacks—0
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies—0
Enhancing Transferability of Adversarial Examples with Spatial Momentum—0
Input-specific Attention Subnetworks for Adversarial Detection—0
Exploring High-Order Structure for Robust Graph Structure Learning—0
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement—0
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and DefenseCode0
DTA: Physical Camouflage Attacks using Differentiable Transformation Network—0
RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression—0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—0
Efficient universal shuffle attack for visual object tracking—0
Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training—0
Block-Sparse Adversarial Attack to Fool Transformer-Based Text ClassifiersCode0
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems—0
A^3D: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks—0
Art-Attack: Black-Box Adversarial Attack via Evolutionary Art—0
Detecting Adversarial Perturbations in Multi-Task PerceptionCode0
Adversarial attacks on neural networks through canonical Riemannian foliationsCode0
Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey—0
Critical Checkpoints for Evaluating Defence Models Against Adversarial Attack and Robustness—0
Debiasing Backdoor Attack: A Benign Application of Backdoor Attack in Eliminating Data Bias—0
Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing—0
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack—0
Attacking c-MARL More Effectively: A Data Driven Approach—0
Adversarial Attack and Defense for Non-Parametric Two-Sample TestsCode0
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons—0
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses—0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods—0
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Robust Unpaired Single Image Super-Resolution of Faces—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—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