157 citations · 314 across the 19 of their papers we have counts for
20 papers
Detecting Adversarial Patches with Class Conditional Reconstruction Networks
Perry Deng, Mohammad Saidur Rahman, Matthew Wright
Defending against physical adversarial attacks is a rapidly growing topic in deep learning and computer vision. Prominent forms of physical adversarial attacks, such as overlaid ad…
Weaponizing Unicodes with Deep Learning -- Identifying Homoglyphs with Weakly Labeled Data
Perry Deng, Cooper Linsky, Matthew Wright
Visually similar characters, or homoglyphs, can be used to perform social engineering attacks or to evade spam and plagiarism detectors. It is thus important to understand the capa…
They Might NOT Be Giants: Crafting Black-Box Adversarial Examples with Fewer Queries Using Particle Swarm Optimization
Rayan Mosli, Matthew Wright, Bo Yuan +1
Machine learning models have been found to be susceptible to adversarial examples that are often indistinguishable from the original inputs. These adversarial examples are created…
Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces
Mohammad Saidur Rahman, Mohsen Imani, Nate Mathews +1
Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity, even when the traffic is protected by a V…
Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks
Mohammad Saidur Rahman, Payap Sirinam, Nate Mathews +2
A passive local eavesdropper can leverage Website Fingerprinting (WF) to deanonymize the web browsing activity of Tor users. The value of timing information to WF has often been di…
Towards Predicting Efficient and Anonymous Tor Circuits
Armon Barton, Mohsen Imani, Jiang Ming +1
The Tor anonymity system provides online privacy for millions of users, but it is slower than typical web browsing. To improve Tor performance, we propose PredicTor, a path selecti…