6 citations · 13 across the 4 of their papers we have counts for
4 papers
Ternary Hashing
Chang Liu, Lixin Fan, Kam Woh Ng +5
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the…
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
Yilun Jin, Lixin Fan, Kam Woh Ng +2
Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed. While adversarial training (AT) is regarded as the most robust defen…
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…