237 citations · 552 across the 17 of their papers we have counts for
8 papers · 1 filter
StyleAT: Defending Face Recognition Against Semantic Attacks
Ben Shapira, Roi Cohen, Shang-Tse Chen +1
With face-recognition models now embedded in everyday authentication and surveillance, recent works have pinpointed a critical weakness: these models remain acutely vulnerable to a…
AngelFingerprint: A Traceable, Explainable, and White-Box Stealthy Watermark for Text-Guided Image Editing
Bo-Han Kung, Futa Waseda, Ching-Chun Chang +2
Text-guided diffusion editing raises disinformation concerns, making reliable image provenance essential. While watermarks are commonly used for this purpose, most methods carry a…
Enhancing Targeted Attack Transferability via Diversified Weight Pruning
Hung-Jui Wang, Yu-Yu Wu, Shang-Tse Chen
Malicious attackers can generate targeted adversarial examples by imposing tiny noises, forcing neural networks to produce specific incorrect outputs. With cross-model transferabil…
UnMask: Adversarial Detection and Defense Through Robust Feature Alignment
Scott Freitas, Shang-Tse Chen, Zijie J. Wang +1
Deep learning models are being integrated into a wide range of high-impact, security-critical systems, from self-driving cars to medical diagnosis. However, recent research has dem…
Talk Proposal: Towards the Realistic Evaluation of Evasion Attacks using CARLA
Cory Cornelius, Shang-Tse Chen, Jason Martin +1
In this talk we describe our content-preserving attack on object detectors, ShapeShifter, and demonstrate how to evaluate this threat in realistic scenarios. We describe how we use…
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
Shang-Tse Chen, Cory Cornelius, Jason Martin +1
Given the ability to directly manipulate image pixels in the digital input space, an adversary can easily generate imperceptible perturbations to fool a Deep Neural Network (DNN) i…
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen +5
The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. This…
Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen +4
Deep neural networks (DNNs) have achieved great success in solving a variety of machine learning (ML) problems, especially in the domain of image recognition. However, recent resea…