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Tamas Abraham

8 accepted papers

2026

Certified but Fooled! Breaking Certified Defenses with Ghost Certificates

Quoc Viet Vo, Tashreque Mohammed Haq, Paul Montague, Tamas Abraham, Ehsan Abbasnejad, Damith C. Ranasinghe
AAAI 2026technical

Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better understand the limits of guarantee provisions. Now, the objective is to not only mislead a classifier, but also to manipulate the certification process t

Cited by 0Source ↗PDF ↗Scholar ↗
2026

Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment

Anh Tuan Bui, Thuy-Trang Vu, Trung Le, Junae Kim, Tamas Abraham, Rollin Omari +2
ICLR 2026poster

In this paper, we investigate the semantic collapsing problem in generative personalization, an under-explored topic where the learned visual concept ($V$) gradually shifts from its original textual meaning and comes to dominate other concepts in multi-concept input prompts. This issue not only redu…

Cited by 0Source ↗code ↗Scholar ↗
2025

Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them

Anh Tuan Bui, Thuy-Trang Vu, Long Tung Vuong, Trung Le, Paul Montague, Tamas Abraham +2
ICLR 2025poster

Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The common principle of previous works to remove a specific concept is to map it to a fixed generic concept, such as a neutra…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2024

Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation

Anh Tuan Bui, Long Tung Vuong, Khanh Doan, Trung Le, Paul Montague, Tamas Abraham +1
NeurIPS 2024poster

Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A practical solution is to selectively removing target concepts from the model, but this may impact the remaining concepts.…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2023

Feature-Space Bayesian Adversarial Learning Improved Malware Detector Robustness

Bao Gia Doan, Shuiqiao Yang, Paul Montague, Olivier De Vel, Tamas Abraham, Seyit Camtepe +3
AAAI 2023technical

We present a new algorithm to train a robust malware detector. Malware is a prolific problem and malware detectors are a front-line defense. Modern detectors rely on machine learning algorithms. Now, the adversarial objective is to devise alterations to the malware code to decrease the chance of bei…

Cited by 11Source ↗PDF ↗Scholar ↗
2021

Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning

Sandamal Weerasinghe, Tamas Abraham, Tansu Alpcan, Sarah M. Erfani, Christopher Leckie, Benjamin I. P. Rubinstein
IJCAI 2021poster

Nonlinear regression, although widely used in engineering, financial and security applications for automated decision making, is known to be vulnerable to training data poisoning. Targeted poisoning attacks may cause learning algorithms to fit decision functions with poor predictive performance. Thi…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2021

Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness

Anh Tuan Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel, Tamas Abraham +1
AAAI 2021technical

Ensemble-based Adversarial Training is a principled approach to achieve robustness against adversarial attacks. An important technicality of this approach is to control the transferability of adversarial examples between ensemble members. We propose in this work a simple, but effective strategy to c…

Cited by 12Source ↗PDF ↗code ↗Scholar ↗
2020

Improving Adversarial Robustness by Enforcing Local and Global Compactness

Anh Bui, Trung Le, He Zhao, Paul Montague, Olivier deVel, Tamas Abraham +1
ECCV 2020poster

The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defense models against such attacks, adversarial training emerges as the most successful method that consistently resists a wi…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗