SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
This paper proposes a statistical fault injection framework named SENTRY that leverages finite-population sampling theory to provide formal reliability guarantees for Vision Transformers. With only a few thousand samples, it bounds failure rate estimation within 1% error margin at 99% confidence, achieving up to 10,700x reduction in experimental cost. The study reveals a highly non-uniform reliability landscape: only 3% of FP32 bit-flips cause failure, but most of those lead to catastrophic accuracy collapse, with vulnerabilities localized to normalization layers and critical exponent bits in IEEE-754 format.
[2606.07620] SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
[Submitted on 30 May 2026]
Title:SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors
View a PDF of the paper titled SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors, by Pramit Kumar Bhaduri and 5 other authors
View PDF HTML (experimental)
Abstract:With the growth of Vision Transformers in safety-critical domains like autonomous systems and medical imaging, ensuring their reliability against soft errors is paramount. While ViTs offer state-of-the-art accuracy, their massive parameter counts render exhaustive fault injection campaigns infeasible. To bridge this gap, a statistical fault injection framework is presented, leveraging finite-population sampling theory to provide formal reliability guarantees. It is demonstrated that failure rates are bounded within a 1% margin at 99\% confidence using only a few thousand samples, regardless of model scale. This methodology achieves up to a 10,700 times reduction in experimental cost compared to exhaustive approaches, while preserving the ability to localize vulnerabilities across architectural components. Through extensive evaluation of different architectures like ViT-Tiny and ViT-Small, a highly non-uniform reliability landscape is uncovered. It is shown that while only 3% of FP32 bit-flips result in failure, the vast majority of these events lead to catastrophic accuracy collapse. Specific vulnerabilities are localized to normalization layers and critical exponent bits within the IEEE-754 format, providing a mathematical foundation and actionable insights for the design of hardened, edge-deployed ViT architectures.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
Cite as: arXiv:2606.07620 [cs.CV]
(or arXiv:2606.07620v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.07620
arXiv-issued DOI via DataCite
Submission history
From: Mahdi Taheri [view email] [v1] Sat, 30 May 2026 11:14:26 UTC (681 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled SENTRY: Statistical Reliability Analysis of Vision Transformers Under Soft Errors, by Pramit Kumar Bhaduri and 5 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-06
Change to browse by:
cs cs.AI cs.DC cs.LG
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)