Copertina

ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Pintor, Maura, Angioni, Daniele, Sotgiu, Angelo, Demetrio, Luca, Demontis, Ambra, Biggio, Battista, Roli, Fabio
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Cryptography and Security Computer Vision and Pattern Recognition Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
di: Angioni, Daniele, et al.
Pubblicazione: (2024)

Evaluating the Evaluators: Trust in Adversarial Robustness Tests
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2025)

Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness
di: Piras, Giorgio, et al.
Pubblicazione: (2024)

Prototype-Guided Robust Learning against Backdoor Attacks
di: Guo, Wei, et al.
Pubblicazione: (2025)

Regression-aware Continual Learning for Android Malware Detection
di: Ghiani, Daniele, et al.
Pubblicazione: (2025)

Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity
di: Guo, Wei, et al.
Pubblicazione: (2025)

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2024)

Machine Learning Security against Data Poisoning: Are We There Yet?
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2022)

Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells
di: Trizna, Dmitrijs, et al.
Pubblicazione: (2024)

Evaluating Line-level Localization Ability of Learning-based Code Vulnerability Detection Models
di: Pintore, Marco, et al.
Pubblicazione: (2025)

secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python
di: Demetrio, Luca, et al.
Pubblicazione: (2021)

Nebula: Self-Attention for Dynamic Malware Analysis
di: Trizna, Dmitrijs, et al.
Pubblicazione: (2023)

Energy-Latency Attacks via Sponge Poisoning
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2022)

Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples
di: Kozak, Matous, et al.
Pubblicazione: (2024)

Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2021)

Label-efficient Training Updates for Malware Detection over Time
di: Minnei, Luca, et al.
Pubblicazione: (2026)

SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines
di: Ponte, Andrea, et al.
Pubblicazione: (2024)

Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection
di: Ponte, Andrea, et al.
Pubblicazione: (2025)

Buffer-free Class-Incremental Learning with Out-of-Distribution Detection
di: Gupta, Srishti, et al.
Pubblicazione: (2025)

Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
di: Gibert, Daniel, et al.
Pubblicazione: (2024)

BlackCATT: Black-box Collusion Aware Traitor Tracing in Federated Learning
di: Rodríguez-Lois, Elena, et al.
Pubblicazione: (2026)

ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
di: Floris, Giuseppe, et al.
Pubblicazione: (2023)

$σ$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2024)

Out-of-Distribution Detection for Continual Learning: Design Principles and Benchmarking
di: Gupta, Srishti, et al.
Pubblicazione: (2025)

Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
di: Gupta, Srishti, et al.
Pubblicazione: (2024)

Robust image classification with multi-modal large language models
di: Villani, Francesco, et al.
Pubblicazione: (2024)

Efficient Adversarial Input Generation via Neural Net Patching
di: Khan, Tooba, et al.
Pubblicazione: (2022)

Empirical Quantification of Spurious Correlations in Malware Detection
di: Perasso, Bianca, et al.
Pubblicazione: (2025)

Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks
di: Guesmi, Amira, et al.
Pubblicazione: (2024)

Adversarial Training against Location-Optimized Adversarial Patches
di: Rao, Sukrut, et al.
Pubblicazione: (2020)

Robustness Analysis against Adversarial Patch Attacks in Fully Unmanned Stores
di: Na, Hyunsik, et al.
Pubblicazione: (2025)

Defensive Prompt Patch: A Robust and Interpretable Defense of LLMs against Jailbreak Attacks
di: Xiong, Chen, et al.
Pubblicazione: (2024)

Security of Deep Reinforcement Learning for Autonomous Driving: A Survey
di: Demontis, Ambra, et al.
Pubblicazione: (2022)

SwitchPatch: Physical Adversarial Attack Strategy with Switchable Adversarial Objectives
di: Jiang, Hanrui, et al.
Pubblicazione: (2025)

Benchmarking Adversarial Patch Selection and Location
di: Kimhi, Shai, et al.
Pubblicazione: (2025)

Safety Interventions against Adversarial Patches in an Open-Source Driver Assistance System
di: Chen, Cheng, et al.
Pubblicazione: (2025)

PatchCURE: Improving Certifiable Robustness, Model Utility, and Computation Efficiency of Adversarial Patch Defenses
di: Xiang, Chong, et al.
Pubblicazione: (2023)

Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms
di: Villani, Francesco, et al.
Pubblicazione: (2024)

Poisoning ML attack and defenses
di: Battista, Biggio, et al.
Pubblicazione: (2025)

TRAP: Hijacking VLA CoT-Reasoning via Adversarial Patches
di: Huang, Zhengxian, et al.
Pubblicazione: (2026)