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Fnu Suya

4 accepted papers

2026

DASH: A Meta-Attack Framework for Synthesizing Effective and Stealthy Adversarial Examples

Abdullah Al Nomaan Nafi, Habibur Rahaman, Zafaryab Haider, Tanzim Mahfuz, Fnu Suya, Swarup Bhunia +1
CVPR 2026

Numerous techniques have been proposed for generating adversarial examples under strict Lp-norm constraints. However, such norm-bounded examples often fail to align well with human perception, and only a few methods specifically explore perceptually aligned adversarial examples. Moreover, it remains

Cited by 0Source ↗code ↗Scholar ↗
2023

Manipulating Transfer Learning for Property Inference

Yulong Tian, Fnu Suya, Anshuman Suri, Fengyuan Xu, David Evans
CVPR 2023poster

Transfer learning is a popular method for tuning pretrained (upstream) models for different downstream tasks using limited data and computational resources. We study how an adversary with control over an upstream model used in transfer learning can conduct property inference attacks on a victim's tu…

Cited by 10Source ↗PDF ↗code ↗Scholar ↗
2023

What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?

Fnu Suya, Xiao Zhang, Yuan Tian, David Evans
NeurIPS 2023poster

We study indiscriminate poisoning for linear learners where an adversary injects a few crafted examples into the training data with the goal of forcing the induced model to incur higher test error. Inspired by the observation that linear learners on some datasets are able to resist the best known a…

Cited by 2Source ↗PDF ↗Scholar ↗
2021

Model-Targeted Poisoning Attacks with Provable Convergence

Fnu Suya, Saeed Mahloujifar, Anshuman Suri, David Evans, Yuan Tian
ICML 2021spotlight

In a poisoning attack, an adversary who controls a small fraction of the training data attempts to select that data, so a model is induced that misbehaves in a particular way. We consider poisoning attacks against convex machine learning models and propose an efficient poisoning attack designed to i…

Cited by 53Source ↗PDF ↗code ↗Scholar ↗