Skip to main content
archive
Search Submit Donate Log in
Press Enter to search · Advanced search

Computer Science > Computer Vision and Pattern Recognition

arXiv:1911.09058 (cs)
[Submitted on 20 Nov 2019 (v1), last revised 22 Oct 2020 (this version, v2)]

Title:Fine-grained Synthesis of Unrestricted Adversarial Examples

Authors:Omid Poursaeed, Tianxing Jiang, Yordanos Goshu, Harry Yang, Serge Belongie, Ser-Nam Lim
View a PDF of the paper titled Fine-grained Synthesis of Unrestricted Adversarial Examples, by Omid Poursaeed and 5 other authors
View PDF HTML (experimental)
Abstract:We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation. Unlike existing unrestricted attacks that typically hand-craft geometric transformations, we learn stylistic and stochastic modifications leveraging state-of-the-art generative models. This allows us to manipulate an image in a controlled, fine-grained manner without being bounded by a norm threshold. Our approach can be used for targeted and non-targeted unrestricted attacks on classification, semantic segmentation and object detection models. Our attacks can bypass certified defenses, yet our adversarial images look indistinguishable from natural images as verified by human evaluation. Moreover, we demonstrate that adversarial training with our examples improves performance of the model on clean images without requiring any modifications to the architecture. We perform experiments on LSUN, CelebA-HQ and COCO-Stuff as high resolution datasets to validate efficacy of our proposed approach.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:1911.09058 [cs.CV]
  (or arXiv:1911.09058v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.1911.09058
arXiv-issued DOI via DataCite

Submission history

From: Omid Poursaeed [view email]
[v1] Wed, 20 Nov 2019 17:42:12 UTC (8,786 KB)
[v2] Thu, 22 Oct 2020 16:53:26 UTC (40,420 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Fine-grained Synthesis of Unrestricted Adversarial Examples, by Omid Poursaeed and 5 other authors
  • View PDF
  • HTML (experimental)
  • TeX Source
view license

Current browse context:

cs.CV
< prev   |   next >
new | recent | 2019-11
Change to browse by:
cs
cs.CR
cs.LG
stat
stat.ML

References & Citations

  • NASA ADS
  • Google Scholar
  • Semantic Scholar

DBLP - CS Bibliography

listing | bibtex
Omid Poursaeed
Serge J. Belongie
Ser-Nam Lim
Loading...

BibTeX formatted citation

Data provided by:

Bookmark

BibSonomy Reddit

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
  • Author
  • Venue
  • Institution
  • Topic

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?)
We gratefully acknowledge support from our major funders, member institutions, , and all contributors.
About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)
Major funding support from
Simons Foundation Simons Foundation International Schmidt Sciences