How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910990103216128 |
|---|---|
| author | Panebianco, Francesco D'Onghia, Mario Carminati, Stefano Zanero aand Michele |
| author_facet | Panebianco, Francesco D'Onghia, Mario Carminati, Stefano Zanero aand Michele |
| contents | Deep learning systems, critical in domains like autonomous vehicles, are vulnerable to adversarial examples (crafted inputs designed to mislead classifiers). This study investigates black-box adversarial attacks in computer vision. This is a realistic scenario, where attackers have query-only access to the target model. Three properties are introduced to evaluate attack feasibility: robustness to compression, stealthiness to automatic detection, and stealthiness to human inspection. State-of-the-Art methods tend to prioritize one criterion at the expense of others. We propose ECLIPSE, a novel attack method employing Gaussian blurring on sampled gradients and a local surrogate model. Comprehensive experiments on a public dataset highlight ECLIPSE's advantages, demonstrating its contribution to the trade-off between the three properties. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05382 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World Panebianco, Francesco D'Onghia, Mario Carminati, Stefano Zanero aand Michele Cryptography and Security Artificial Intelligence Deep learning systems, critical in domains like autonomous vehicles, are vulnerable to adversarial examples (crafted inputs designed to mislead classifiers). This study investigates black-box adversarial attacks in computer vision. This is a realistic scenario, where attackers have query-only access to the target model. Three properties are introduced to evaluate attack feasibility: robustness to compression, stealthiness to automatic detection, and stealthiness to human inspection. State-of-the-Art methods tend to prioritize one criterion at the expense of others. We propose ECLIPSE, a novel attack method employing Gaussian blurring on sampled gradients and a local surrogate model. Comprehensive experiments on a public dataset highlight ECLIPSE's advantages, demonstrating its contribution to the trade-off between the three properties. |
| title | How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2506.05382 |