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06/15/2023
Knowledge Assembly: Semi-Supervised Multi-Task Learning from Multiple Datasets with Disjoint Labels
In real-world scenarios we often need to perform multiple tasks simultan...
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08/19/2022
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention
Improving multi-view aggregation is integral for multi-view pedestrian d...
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08/04/2022
Privacy Safe Representation Learning via Frequency Filtering Encoder
Deep learning models are increasingly deployed in real-world application...
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03/30/2022
Investigating Top-k White-Box and Transferable Black-box Attack
Existing works have identified the limitation of top-1 attack success ra...
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10/06/2021
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
Convolutional Neural Networks (CNNs) have become the de facto gold stand...
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04/07/2021
Universal Adversarial Training with Class-Wise Perturbations
Despite their overwhelming success on a wide range of applications, conv...
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03/02/2021
A Brief Survey on Deep Learning Based Data Hiding, Steganography and Watermarking
Data hiding is the art of concealing messages with limited perceptual ch...
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03/02/2021
A Survey On Universal Adversarial Attack
Deep neural networks (DNNs) have demonstrated remarkable performance for...
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02/12/2021
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
The booming interest in adversarial attacks stems from a misalignment be...
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12/30/2020
Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach
Data hiding is one widely used approach for protecting authentication an...
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10/26/2020
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy
Recently, convolutional neural networks (CNNs) have made significant adv...
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10/23/2020
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
ResNet or DenseNet? Nowadays, most deep learning based approaches are im...
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10/07/2020
Revisiting Batch Normalization for Improving Corruption Robustness
Modern deep neural networks (DNN) have demonstrated remarkable success i...
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10/07/2020
Batch Normalization Increases Adversarial Vulnerability: Disentangling Usefulness and Robustness of Model Features
Batch normalization (BN) has been widely used in modern deep neural netw...
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10/07/2020
CD-UAP: Class Discriminative Universal Adversarial Perturbation
A single universal adversarial perturbation (UAP) can be added to all na...
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10/07/2020
Double Targeted Universal Adversarial Perturbations
Despite their impressive performance, deep neural networks (DNNs) are wi...
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07/13/2020
Data from Model: Extracting Data from Non-robust and Robust Models
The essence of deep learning is to exploit data to train a deep neural n...
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07/13/2020
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations
A wide variety of works have explored the reason for the existence of ad...
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01/02/2020
Fast Perception, Planning, and Execution for a Robotic Butler: Wheeled Humanoid M-Hubo
As the aging population grows at a rapid rate, there is an ever growing ...
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07/30/2019
