591 citations · 1.2k across the 45 of their papers we have counts for
6 papers · 1 filter
Decompiling x86 Deep Neural Network Executables
Zhibo Liu, Yuanyuan Yuan, Shuai Wang +2
Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitiv…
Generating Adversarial Examples with Controllable Non-transferability
Renzhi Wang, Tianwei Zhang, Xiaofei Xie +4
Adversarial attacks against Deep Neural Networks have been widely studied. One significant feature that makes such attacks particularly powerful is transferability, where the adver…
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
Jianwen Sun, Tianwei Zhang, Xiaofei Xie +4
Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasib…
Oracle-Supported Dynamic Exploit Generation for Smart Contracts
Haijun Wang, Yi Li, Shang-Wei Lin +3
Despite the high stakes involved in smart contracts, they are often developed in an undisciplined manner, leaving the security and reliability of blockchain transactions at risk. I…
FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces
Run Wang, Felix Juefei-Xu, Lei Ma +4
In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial imag…
DeepLaser: Practical Fault Attack on Deep Neural Networks
Jakub Breier, Xiaolu Hou, Dirmanto Jap +3
As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a…