Publications (7)
Improved Network Robustness with Adversary Critic
Alexander Matyasko, Lap-Pui Chau
Ideally, what confuses neural network should be confusing to humans. However, recent experiments have shown that small, imperceptible perturbations can change the network predictio…
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini +23
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be us…
PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack
Alexander Matyasko, Lap-Pui Chau
State-of-the-art deep neural networks are sensitive to small input perturbations. Since the discovery of this intriguing vulnerability, many defence methods have been proposed that…
TsallisPGD: Adaptive Gradient Weighting for Adversarial Attacks on Semantic Segmentation
Alexander Matyasko, Xin Lou, Indriyati Atmosukarto +1
Attacking semantic segmentation models is significantly harder than image classification models because an attacker must flip thousands of pixel predictions simultaneously. Standar…
Training-Free Action Recognition and Goal Inference with Dynamic Frame Selection
Ee Yeo Keat, Zhang Hao, Alexander Matyasko +1
We introduce VidTFS, a Training-free, open-vocabulary video goal and action inference framework that combines the frozen vision foundational model (VFM) and large language model (L…
Dissecting Multimodality in VideoQA Transformer Models by Impairing Modality Fusion
Ishaan Singh Rawal, Alexander Matyasko, Shantanu Jaiswal +2
While VideoQA Transformer models demonstrate competitive performance on standard benchmarks, the reasons behind their success are not fully understood. Do these models capture the…
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy
Alex Lamb, Vikas Verma, Kenji Kawaguchi +4
Adversarial robustness has become a central goal in deep learning, both in the theory and the practice. However, successful methods to improve the adversarial robustness (such as a…