Demo Data from EDBT 2026 / reviewers in the wild / expert
Tashreque Mohammed Haq
dblp:232/9771
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning › robustness
adversarial examples |
1.0 | 1 | 2026 | Certified but Fooled! Breaking Certified Defenses with Ghost Certificates · AAAI 2026 |
Machine learning › Trustworthy machine learning
adversarial machine learning |
1.0 | 1 | 2026 | Certified but Fooled! Breaking Certified Defenses with Ghost Certificates · AAAI 2026 |
Machine learning › Trustworthy machine learning › robustness
certified robustness |
1.0 | 1 | 2026 | Certified but Fooled! Breaking Certified Defenses with Ghost Certificates · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
region-focused adversarial perturbation · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Certified but Fooled! Breaking Certified Defenses with Ghost CertificatesabstractCertified 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 to generate a robustness guarantee for an adversarial input—certificate spoofing. A recent study in ICLR demonstrated that crafting large perturbations can shift inputs far into regions capable of generating a certificate for an incorrect class. Our study investigates if perturbations are needed to cause a misclassification and yet coax a certified model into issuing a deceptive, large robustness radius for a target class can still be made small and imperceptible. We explore the idea of region-focused adversarial examples to craft imperceptible perturbations, spoof certificates and achieve certification radii larger than the source class—ghost certificates. Extensive evaluations with the ImageNet demonstrate the ability to effectively bypass state-of-the-art certified defenses such as Densepure. Our work underscores the need to better understand the limits of robustness certification methods. Viet Quoc Vo, Tashreque Mohammed Haq, Paul Montague, Tamas Abraham, Ehsan Abbasnejad, Damith Chinthana Ranasinghe |
AAAI | 2 |