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

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Showing 1251–1300 of 1808 papers

TitleStatusHype
Gradient-guided Unsupervised Text Style Transfer via Contrastive Learning—0
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense—0
GradMDM: Adversarial Attack on Dynamic Networks—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
GraphMU: Repairing Robustness of Graph Neural Networks via Machine Unlearning—0
Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents—0
GreedyPixel: Fine-Grained Black-Box Adversarial Attack Via Greedy Algorithm—0
Hardware Accelerator for Adversarial Attacks on Deep Learning Neural Networks—0
Harmonic Adversarial Attack Method—0
Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning—0
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds—0
Hessian-Aware Zeroth-Order Optimization for Black-Box Adversarial Attack—0
Heterogeneous Architecture Search Approach within Adversarial Dynamic Defense Framework—0
Heterogeneous Multi-Player Multi-Armed Bandits Robust To Adversarial Attacks—0
HGAttack: Transferable Heterogeneous Graph Adversarial Attack—0
Hiding Backdoors within Event Sequence Data via Poisoning Attacks—0
Hijacking Vision-and-Language Navigation Agents with Adversarial Environmental Attacks—0
Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems—0
Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case—0
How Sampling Impacts the Robustness of Stochastic Neural Networks—0
Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack—0
Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models—0
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks—0
I2VGuard: Safeguarding Images against Misuse in Diffusion-based Image-to-Video Models—0
Identification of Attack-Specific Signatures in Adversarial Examples—0
Identification of Systematic Errors of Image Classifiers on Rare Subgroups—0
Identifying Classes Susceptible to Adversarial Attacks—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection—0
IDT: Dual-Task Adversarial Attacks for Privacy Protection—0
ILFO: Adversarial Attack on Adaptive Neural Networks—0
Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks—0
Image-based Multimodal Models as Intruders: Transferable Multimodal Attacks on Video-based MLLMs—0
ImF: Implicit Fingerprint for Large Language Models—0
Impact of Scaled Image on Robustness of Deep Neural Networks—0
Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary—0
Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks—0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability—0
Improved Adversarial Training via Learned Optimizer—0
Improving adversarial robustness of deep neural networks by using semantic information—0
Enhancing Transferability of Adversarial Examples with Spatial Momentum—0
Improving Adversarial Transferability with Scheduled Step Size and Dual Example—0
Improving Deep Learning Model Robustness Against Adversarial Attack by Increasing the Network Capacity—0
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder—0
Improving Network Interpretability via Explanation Consistency Evaluation—0
Improving Neural Network Robustness through Neighborhood Preserving Layers—0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator—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