activity
20222026
most citedAdversarial Detection: Attacking Object Detection in Real Time

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

collaborators

5 papers

cs.CV2026

Reliable Egocentric Action Anticipation via Temporal Reliability Suppression and Compositional Graph Decoding

Mahsa Mohammadi, Sareh Rowlands

Wearable action anticipation systems must remain reliable despite missing frames, masking, and sensor noise, yet existing egocentric anticipation methods largely assume clean obser…

cs.CV2025

Bridging Domain Gaps for Fine-Grained Moth Classification Through Expert-Informed Adaptation and Foundation Model Priors

Ross J Gardiner, Guillaume Mougeot, Sareh Rowlands +3

Labelling images of Lepidoptera (moths) from automated camera systems is vital for understanding insect declines. However, accurate species identification is challenging due to dom…

cs.LG2022

Distributed Black-box Attack: Do Not Overestimate Black-box Attacks

Han Wu, Sareh Rowlands, Johan Wahlstrom

As cloud computing becomes pervasive, deep learning models are deployed on cloud servers and then provided as APIs to end users. However, black-box adversarial attacks can fool ima…

cs.AI2022★ 5 cited

Adversarial Detection: Attacking Object Detection in Real Time

Han Wu, Syed Yunas, Sareh Rowlands +2

Intelligent robots rely on object detection models to perceive the environment. Following advances in deep learning security it has been revealed that object detection models are v…

cs.RO2022

A Human-in-the-Middle Attack against Object Detection Systems

Han Wu, Sareh Rowlands, Johan Wahlstrom

Object detection systems using deep learning models have become increasingly popular in robotics thanks to the rising power of CPUs and GPUs in embedded systems. However, these mod…