36 citations · 194 across the 34 of their papers we have counts for
11 papers · 1 filter
EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images
Prithviraj Dhar, Amit Kumar, Kirsten Kaplan +3
A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency…
Max-Margin Contrastive Learning
Anshul Shah, Suvrit Sra, Rama Chellappa +1
Standard contrastive learning approaches usually require a large number of negatives for effective unsupervised learning and often exhibit slow convergence. We suspect this behavio…
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Chun Pong Lau, Jiang Liu, Hossein Souri +3
Adversarial training (AT) is considered to be one of the most reliable defenses against adversarial attacks. However, models trained with AT sacrifice standard accuracy and do not…
Mutual Adversarial Training: Learning together is better than going alone
Jiang Liu, Chun Pong Lau, Hossein Souri +2
Recent studies have shown that robustness to adversarial attacks can be transferred across networks. In other words, we can make a weak model more robust with the help of a strong…
HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet
Cheng Peng, Andriy Myronenko, Ali Hatamizadeh +6
Semantic segmentation of 3D medical images is a challenging task due to the high variability of the shape and pattern of objects (such as organs or tumors). Given the recent succes…
Distill and De-bias: Mitigating Bias in Face Verification using Knowledge Distillation
Prithviraj Dhar, Joshua Gleason, Aniket Roy +3
Face recognition networks generally demonstrate bias with respect to sensitive attributes like gender, skintone etc. For gender and skintone, we observe that the regions of the fac…
Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
Jiang Liu, Alexander Levine, Chun Pong Lau +2
Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of…
Self-Denoising Neural Networks for Few Shot Learning
Steven Schwarcz, Sai Saketh Rambhatla, Rama Chellappa
In this paper, we introduce a new architecture for few shot learning, the task of teaching a neural network from as few as one or five labeled examples. Inspired by the theoretical…
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch
Hossein Souri, Liam Fowl, Rama Chellappa +2
As the curation of data for machine learning becomes increasingly automated, dataset tampering is a mounting threat. Backdoor attackers tamper with training data to embed a vulnera…
The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory
Sai Saketh Rambhatla, Rama Chellappa, Abhinav Shrivastava
We tackle object category discovery, which is the problem of discovering and localizing novel objects in a large unlabeled dataset. While existing methods show results on datasets…
Cortical Features for Defense Against Adversarial Audio Attacks
Ilya Kavalerov, Ruijie Zheng, Wojciech Czaja +1
We propose using a computational model of the auditory cortex as a defense against adversarial attacks on audio. We apply several white-box iterative optimization-based adversarial…