167 citations · 577 across the 21 of their papers we have counts for
25 papers
Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack
Sze Jue Yang, Quang Nguyen, Chee Seng Chan +1
The vulnerabilities to backdoor attacks have recently threatened the trustworthiness of machine learning models in practical applications. Conventional wisdom suggests that not eve…
Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection
Jian Han Lim, Chee Seng Chan, Kam Woh Ng +2
By and large, existing Intellectual Property (IP) protection on deep neural networks typically i) focus on image classification task only, and ii) follow a standard digital waterma…
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
Lixin Fan, Kam Woh Ng, Ce Ju +4
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the…
[Extended version] Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
Lixin Fan, Kam Woh Ng, Chee Seng Chan
With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect t…
ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT
Yipeng Sun, Zihan Ni, Chee-Kheng Chng +9
Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavi…
ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)
Chee-Kheng Chng, Yuliang Liu, Yipeng Sun +11
This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recogn…