78 citations · 274 across the 28 of their papers we have counts for
6 papers · 1 filter
Adaptive Attractors: A Defense Strategy against ML Adversarial Collusion Attacks
Jiyi Zhang, Han Fang, Ee-Chien Chang
In the seller-buyer setting on machine learning models, the seller generates different copies based on the original model and distributes them to different buyers, such that advers…
Finding Meaningful Distributions of ML Black-boxes under Forensic Investigation
Jiyi Zhang, Han Fang, Hwee Kuan Lee +1
Given a poorly documented neural network model, we take the perspective of a forensic investigator who wants to find out the model's data domain (e.g. whether on face images or tra…
Purifier: Defending Data Inference Attacks via Transforming Confidence Scores
Ziqi Yang, Lijin Wang, Da Yang +5
Neural networks are susceptible to data inference attacks such as the membership inference attack, the adversarial model inversion attack and the attribute inference attack, where…
SHADOWCAST: Controllable Graph Generation
Wesley Joon-Wie Tann, Ee-Chien Chang, Bryan Hooi
We introduce the controllable graph generation problem, formulated as controlling graph attributes during the generative process to produce desired graphs with understandable struc…
Enhancing Transformation-based Defenses using a Distribution Classifier
Connie Kou, Hwee Kuan Lee, Ee-Chien Chang +1
Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms. Stochastic input tra…
Flipped-Adversarial AutoEncoders
Jiyi Zhang, Hung Dang, Hwee Kuan Lee +1
We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously trains a generative model G that maps an arbitrary latent code distribution to a data distribution and an en…