Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets
This research uncovers naturally occurring statistical signals in vision data that can be exploited like backdoor triggers without malicious insertion. By analyzing ImageNet, the authors identify patterns strongly linked to specific labels, use statistical controls to remove spurious correlations, and demonstrate that these signals directly and predictably alter model predictions. These statistical adversaries are more targeted than generic corruptions and transfer across architectures, suggesting vulnerabilities stem from dataset structure rather than model idiosyncrasies. The study recommends treating spurious structure as a latent attack surface.
-->
[Submitted on 6 Jul 2026]
Title:Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets
View a PDF of the paper titled Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets, by Paul K. Mandal and 2 other authors
View PDF HTML (experimental)
Abstract:Model-specific adversarial attacks have been extensively studied. We study a different failure mode: naturally occurring statistical signals in vision data that can behave like backdoor-like triggers without being maliciously inserted. We call these signals statistical adversaries. We analyse Imagenet to find patterns that are strongly linked to certain labels. We then use statistical controls to remove random correlations from our candidate signals. Finally, we demonstrate that these signals directly and predictably alter model predictions. These statistical adversaries are more targeted than generic corruptions and transfer across different model architectures. This suggests that some vulnerabilities are driven by dataset structure and distribution rather than a single model's idiosyncrasies. We conclude that ordinary datasets can contain exploitable adversarial surfaces even in the absence of poisoning, and suggest that dataset audits should treat spurious structure not only as a source of bias or interpretability failure, but also as a latent attack surface for vision models.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
MSC classes: 68T07, 68T45, 62H30, 62H35
ACM classes: I.2.6; I.2.10; I.4.8; I.5.2; I.5.4; G.3
Cite as: arXiv:2607.05516 [cs.CV]
(or arXiv:2607.05516v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.05516
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Paul K. Mandal [view email] [v1] Mon, 6 Jul 2026 18:00:25 UTC (3,092 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets, by Paul K. Mandal and 2 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-07
Change to browse by:
cs cs.AI cs.CR 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?)