45 citations · 108 across the 14 of their papers we have counts for
15 papers
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…
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…
Image Shortcut Squeezing: Countering Perturbative Availability Poisons with Compression
Zhuoran Liu, Zhengyu Zhao, Martha Larson
Perturbative availability poisons (PAPs) add small changes to images to prevent their use for model training. Current research adopts the belief that practical and effective approa…
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…
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection
Hamid Bostani, Zhengyu Zhao, Zhuoran Liu +1
Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection (AMD); however, ML models are vulnerable to realistic evasion attacks--crafting realizable Adver…
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples
Zhuoran Liu, Zhengyu Zhao, Alex Kolmus +4
Recent work has shown that imperceptible perturbations can be applied to craft unlearnable examples (ULEs), i.e. images whose content cannot be used to improve a classifier during…
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…
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…
Adversarial Image Color Transformations in Explicit Color Filter Space
Zhengyu Zhao, Zhuoran Liu, Martha Larson
Deep Neural Networks have been shown to be vulnerable to adversarial images. Conventional attacks strive for indistinguishable adversarial images with strictly restricted perturbat…
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…
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter
Zhengyu Zhao, Zhuoran Liu, Martha Larson
We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification. Our approach, Adversar…
Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance
Zhengyu Zhao, Zhuoran Liu, Martha Larson
The success of image perturbations that are designed to fool image classifier is assessed in terms of both adversarial effect and visual imperceptibility. The conventional assumpti…
Non-Determinism in Neural Networks for Adversarial Robustness
Daanish Ali Khan, Linhong Li, Ninghao Sha +4
Recent breakthroughs in the field of deep learning have led to advancements in a broad spectrum of tasks in computer vision, audio processing, natural language processing and other…
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval
Zhuoran Liu, Zhengyu Zhao, Martha Larson
An adversarial query is an image that has been modified to disrupt content-based image retrieval (CBIR) while appearing nearly untouched to the human eye. This paper presents an an…
Exploiting Unlabeled Data for Neural Grammatical Error Detection
Zhuoran Liu, Yang Liu
Identifying and correcting grammatical errors in the text written by non-native writers has received increasing attention in recent years. Although a number of annotated corpora ha…