activity
20192026
most citedWho's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval

5 citations · 12 across the 4 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Dynamic Cluster Data Sampling for Efficient and Long-Tail-Aware Vision-Language Pre-training

Mingliang Liang, Zhuoran Liu, Arjen P. de Vries +1

The computational cost of training a vision-language model (VLM) can be reduced by sampling the training data. Previous work on efficient VLM pre-training has pointed to the import…

eess.AS2023

Beyond Neural-on-Neural Approaches to Speaker Gender Protection

Loes van Bemmel, Zhuoran Liu, Nik Vaessen +1

Recent research has proposed approaches that modify speech to defend against gender inference attacks. The goal of these protection algorithms is to control the availability of inf…

cs.LG20221 cited

Generative Poisoning Using Random Discriminators

Dirren van Vlijmen, Alex Kolmus, Zhuoran Liu +2

We introduce ShortcutGen, a new data poisoning attack that generates sample-dependent, error-minimizing perturbations by learning a generator. The key novelty of ShortcutGen is the…

cs.LG2020

On Success and Simplicity: A Second Look at Transferable Targeted Attacks

Zhengyu Zhao, Zhuoran Liu, Martha Larson

Achieving transferability of targeted attacks is reputed to be remarkably difficult. Currently, state-of-the-art approaches are resource-intensive because they necessitate training…

cs.CR20205 cited

Screen Gleaning: A Screen Reading TEMPEST Attack on Mobile Devices Exploiting an Electromagnetic Side Channel

Zhuoran Liu, Niels Samwel, Léo Weissbart +4

We introduce screen gleaning, a TEMPEST attack in which the screen of a mobile device is read without a visual line of sight, revealing sensitive information displayed on the phone…

cs.IR2020

Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start

Zhuoran Liu, Martha Larson

E-commerce platforms provide their customers with ranked lists of recommended items matching the customers' preferences. Merchants on e-commerce platforms would like their items to…