20 citations · 77 across the 12 of their papers we have counts for
12 papers
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks
Xu Han, Anmin Liu, Chenxuan Yao +2
Deep neural networks are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to the benign input. After achieving nearly 100% attack…
MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation
Kun Han, Yifeng Xiong, Chenyu You +5
Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical imag…
Semantic Adversarial Attacks on Face Recognition through Significant Attributes
Yasmeen M. Khedr, Yifeng Xiong, Kun He
Face recognition is known to be vulnerable to adversarial face images. Existing works craft face adversarial images by indiscriminately changing a single attribute without being aw…
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks
Xu Han, Anmin Liu, Yifeng Xiong +2
Deep neural networks have shown to be very vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to benign inputs. After achieving impressive attac…
The Accuracy vs. Sampling Overhead Trade-off in Quantum Error Mitigation Using Monte Carlo-Based Channel Inversion
Yifeng Xiong, Soon Xin Ng, Lajos Hanzo
Quantum error mitigation (QEM) is a class of promising techniques for reducing the computational error of variational quantum algorithms. In general, the computational error reduct…
Dual-Frequency Quantum Phase Estimation Mitigates the Spectral Leakage of Quantum Algorithms
Yifeng Xiong, Soon Xin Ng, Gui-Lu Long +1
Quantum phase estimation is an important component in diverse quantum algorithms. However, it suffers from spectral leakage, when the reciprocal of the record length is not an inte…
Circuit Symmetry Verification Mitigates Quantum-Domain Impairments
Yifeng Xiong, Daryus Chandra, Soon Xin Ng +1
State-of-the-art noisy intermediate-scale quantum computers require low-complexity techniques for the mitigation of computational errors inflicted by quantum decoherence. Symmetry…
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability
Yifeng Xiong, Jiadong Lin, Min Zhang +2
The black-box adversarial attack has attracted impressive attention for its practical use in the field of deep learning security. Meanwhile, it is very challenging as there is no a…
Detecting Textual Adversarial Examples through Randomized Substitution and Vote
Xiaosen Wang, Yifeng Xiong, Kun He
A line of work has shown that natural text processing models are vulnerable to adversarial examples. Correspondingly, various defense methods are proposed to mitigate the threat of…
Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection
Jingjing Cui, Yifeng Xiong, Soon Xin Ng +1
Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices, where quantum approximation optimization algorithms (QAOAs) constitute pro…
Quantum Error Mitigation Relying on Permutation Filtering
Yifeng Xiong, Soon Xin Ng, Lajos Hanzo
Quantum error mitigation (QEM) is a class of promising techniques capable of reducing the computational error of variational quantum algorithms tailored for current noisy intermedi…
Sampling Overhead Analysis of Quantum Error Mitigation: Uncoded vs. Coded Systems
Yifeng Xiong, Daryus Chandra, Soon Xin Ng +1
Quantum error mitigation (QEM) is a promising technique of protecting hybrid quantum-classical computation from decoherence, but it suffers from sampling overhead which erodes the…