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 901–950 of 1808 papers

TitleStatusHype
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
Agents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks—0
How Sampling Impacts the Robustness of Stochastic Neural Networks—0
Securing the Diagnosis of Medical Imaging: An In-depth Analysis of AI-Resistant Attacks—0
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning—0
AGSOA:Graph Neural Network Targeted Attack Based on Average Gradient and Structure Optimization—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
Using Word Embeddings to Explore the Learned Representations of Convolutional Neural Networks—0
Identifying Classes Susceptible to Adversarial Attacks—0
Identifying Informative Latent Variables Learned by GIN via Mutual Information—0
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception—0
IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection—0
IDT: Dual-Task Adversarial Attacks for Privacy Protection—0
A Generative Victim Model for Segmentation—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
A Generative Adversarial Attack for Multilingual Text Classifiers—0
Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networks—0
TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification—0
Imperceptible Physical Attack against Face Recognition Systems via LED Illumination Modulation—0
Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability—0
TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models—0
Improved Adversarial Training via Learned Optimizer—0
Utilizing Adversarial Targeted Attacks to Boost Adversarial Robustness—0
A General Black-box Adversarial Attack on Graph-based Fake News Detectors—0
Improving adversarial robustness of deep neural networks by using semantic information—0
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection—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
A Framework for Verification of Wasserstein Adversarial Robustness—0
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator—0
The Attack Generator: A Systematic Approach Towards Constructing Adversarial Attacks—0
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations—0
Improving Robustness of Task Oriented Dialog Systems—0
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness—0
A Framework for Understanding Model Extraction Attack and Defense—0
The Dark Side of Dynamic Routing Neural Networks: Towards Efficiency Backdoor Injection—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