Zhuoran Liu

Assistant Professor
Parallel Computing Systems (PCS)
University of Amsterdam

Room L5.42, Lab 42, Amsterdam, 1098 XH

Parallel Computing Systems (PCS) University of Amsterdam
Zhuoran Liu

I am an Assistant Professor in the Parallel Computing Systems (PCS) group at the University of Amsterdam (UvA). Previously, I was a postdoc in the Digital Security group at Radboud University, advised by Prof. Lejla Batina, and in the Systems and Security group at Leiden University, advised by Prof. Nele Mentens. Before that, I was a PhD student in the Data Science group at Radboud University, advised by Prof. Martha Larson.

My research interests are at the intersection of AI and security/privacy. I conduct research on adversarial machine learning and hardware security, with a particular focus on side-channel analysis and fault injection.

Email / GitHub / Google Scholar

Main Publications

Triggering Stealthy Feature Map Backdoors via Physical Fault Injection in Embedded Neural Networks

Steyn Hommes, Vincent Dankbaar, Tanguy Stekke, Xiaomeng Wang, Lisanne Weidmann, Senna van Hoek, Durba Chatterjee, Lejla Batina, Zhuoran Liu

2026

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

Mingliang Liang, Zhuoran Liu, Arjen P. de Vries, Martha Larson

European Conference on Computer Vision (ECCV), 2026

arXiv·Code

MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness

Xiaoyun Xu, Shujian Yu, Zhuoran Liu, Stjepan Picek

Network and Distributed System Security Symposium (NDSS), 2026

arXiv·Code

Focus Session: Exploring Semantic Leakage in Edge FPGA Implementations of Neural Networks

Zhuoran Liu, Konstantina Miteloudi, Durba Chatterjee, Lejla Batina

Design, Automation and Test in Europe Conference (DATE), 2026

IEEE Xplore

Generative AI and the Threat to Thinking

Martha Larson, Zhuoran Liu

IEEE Security & Privacy, 2025

IEEE Xplore

Towards Backdoor Stealthiness in Model Parameter Space

Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas, Stjepan Picek

ACM Conference on Computer and Communications Security (CCS), 2025

Code

Resisting Bag-based Attribute Profiling by Adding Adversarial Items to Existing Media Profiles

Zhuoran Liu, Zhengyu Zhao, Martha Larson

IEEE Transactions on Information Forensics and Security (TIFS), 2025

Code

BAN: Detecting Backdoors Activated by Adversarial Neuron Noise

Xiaoyun Xu, Zhuoran Liu, Stefanos Koffas, Shujian Yu, Stjepan Picek

Advances in Neural Information Processing Systems (NeurIPS), 2024

arXiv·Code

SoK: Neural Network Extraction Through Physical Side Channels

Péter Horváth, Dirk Lauret, Zhuoran Liu, Lejla Batina

USENIX Security Symposium, 2024

Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression

Zhuoran Liu, Zhengyu Zhao, Martha Larson

International Conference on Machine Learning (ICML), 2023

Code

Is Adversarial Training Really a Silver Bullet for Mitigating Data Poisoning?

Rui Wen, Zhengyu Zhao, Zhuoran Liu, Michael Backes, Tianhao Wang, Yang Zhang

International Conference on Learning Representations (ICLR), Spotlight, 2023

OpenReview·Code

Beyond Neural-on-Neural Approaches to Speaker Gender Protection

Loes van Bemmel, Zhuoran Liu, Nik Vaessen, Martha Larson

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023

arXiv

Going Grayscale: The Road to Understanding and Improving Unlearnable Examples

Zhuoran Liu, Zhengyu Zhao, Alex Kolmus, Tijn Berns, Twan van Laarhoven, Tom Heskes, Martha Larson

arXiv, 2021

Code

Textual Concept Expansion with Commonsense Knowledge to Improve Dual-Stream Image-Text Matching

Mingliang Liang, Zhuoran Liu, Martha Larson

International Conference on Multimedia Modeling (MMM), 2023

Code

Generative Poisoning Using Random Discriminators

Dirren van Vlijmen, Alex Kolmus, Zhuoran Liu, Zhengyu Zhao, Martha Larson

Responsible Computer Vision Workshop at European Conference on Computer Vision (ECCV), 2022

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

Zhengyu Zhao, Zhuoran Liu, Martha Larson

Advances in Neural Information Processing Systems (NeurIPS), 2021

Code

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

Zhuoran Liu, Martha Larson

The Web Conference (WWW), 2021

Code·Video·Slides

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

Zhuoran Liu, Niels Samwel, Léo Weissbart, Zhengyu Zhao, Dirk Lauret, Lejla Batina, Martha Larson

Network and Distributed System Security Symposium (NDSS), 2021

Code·Video

Adversarial Robustness Against Image Color Transformation within Parametric Filter Space

Zhengyu Zhao, Zhuoran Liu, Martha Larson

British Machine Vision Conference (BMVC), 2020

Code

Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance

Zhengyu Zhao, Zhuoran Liu, Martha Larson

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

Code

Who’s Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval

Zhuoran Liu, Zhengyu Zhao, Martha Larson

International Conference on Multimedia Retrieval (ICMR), 2019

Code

Other Publications

Joaquín Figueira, Rob Van Gastel, Giacomo D’Amicantonio, Zhuoran Liu, Ioan Gabriel Bucur, Faysal Boughorbel, Egor Bondarev. AOI-SSL: Self-Supervised Framework for Efficient Segmentation of Wire-bonded Semiconductors in Optical Inspection. arXiv, 2026.
Lizzy Grootjen, Zhuoran Liu, Ileana Buhan. MIDSCAN: Investigating the Portability Problem for Cross-Device DL-SCA. International Workshop on Lightweight Cryptography for Security and Privacy (LightSec), 2025.
Zhuoran Liu, Zhengyu Zhao, Martha Larson, Laurent Amsaleg. Exploring Quality Camouflage for Social Images. MediaEval Workshop, 2020.
Zhuoran Liu, Zhengyu Zhao, Martha Larson. Pixel Privacy 2019: Protecting Sensitive Scene Information in Images. MediaEval Workshop, 2019.
Zhuoran Liu, Zhengyu Zhao. Adversarial Photo Frame: Concealing Sensitive Scene Information of Social Images in a User-Acceptable Manner. MediaEval Workshop, 2019. [Slides]
Zhengyu Zhao, Zhuoran Liu, Martha Larson, Ahmet Iscen, Naoko Nitta. Reproducible Experiments on Adaptive Discriminative Region Discovery for Scene Recognition. Reproducibility@ACM MM, 2019. [Code]
Martha Larson, Zhuoran Liu, Simon Brugman, Zhengyu Zhao. Pixel Privacy: Increasing Image Appeal while Blocking Automatic Inference of Sensitive Scene Information. MediaEval Workshop, 2018. [Slides]
Zhuoran Liu, Zhengyu Zhao. First Steps in Pixel Privacy: Exploring Deep Learning-based Image Enhancement against Large-Scale Image Inference. MediaEval Workshop, 2018.

Service

Program committee / reviewer: AAAI, AISTATS, CHES, CVPR, DAC, ECCV, FAccT, ICCV, ICML, ICLR, NeurIPS, TIFS, TDSC, TPAMI, USENIX Security, etc.

External reviewer: IEEE S&P, NDSS

Other: Top Reviewer, NeurIPS 2024

Organizing committee: Pixel Privacy Task @ MediaEval, ACM Multimedia