ImageNet

ImageNet is a large-scale dataset containing millions of labeled images used to evaluate the performance of machine learning models, particularly in the context of image classification tasks.

Papers using ImageNet (20)

Perturbation Analysis Of Gradient-based Adversarial Attacks2020 · 11 citesGI-PIP: Do We Require Impractical Auxiliary Dataset For Gradient Inversion Attacks?2024 · 8 citesParsimonious Black-box Adversarial Attacks Via Efficient Combinatorial Optimization2019 · 55 citesRPP: A Certified Poisoned-sample Detection Framework For Backdoor Attacks Under Dataset Imbalance2026How Worst-case Are Adversarial Attacks? Linking Adversarial And Perturbation Robustness2026Pubdef: Defending Against Transfer Attacks From Public Models2023DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples2025Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks2024Adversarial Logit Pairing2018 · 467 citesBlack-box Adversarial Attacks With Limited Queries And Information2018 · 326 citesThere Are No Bit Parts For Sign Bits In Black-box Attacks2019 · 14 citesAttacking Deep Networks With Surrogate-based Adversarial Black-box Methods Is Easy2022 · 10 citesBenchmarking Robustness To Adversarial Image Obfuscations2023 · 2 citesBeating Attackers At Their Own Games: Adversarial Example Detection Using Adversarial Gradient Directions2020 · 1 citesCounter-samples: A Stateless Strategy To Neutralize Black Box Adversarial Attacks2024Puridefense: Randomized Local Implicit Adversarial Purification For Defending Black-box Query-based Attacks2024Unlearn And Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy2024Theoretical Corrections And The Leveraging Of Reinforcement Learning To Enhance Triangle Attack2024On Practical Aspects Of Aggregation Defenses Against Data Poisoning Attacks2023BO-DBA: Query-Efficient Decision-Based Adversarial Attacks via Bayesian Optimization2021 · 1 cites