87 citations · 168 across the 18 of their papers we have counts for
21 papers
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
Knowledge Assembly: Semi-Supervised Multi-Task Learning from Multiple Datasets with Disjoint Labels
Federica Spinola, Philipp Benz, Minhyeong Yu +1
In real-world scenarios we often need to perform multiple tasks simultaneously. Multi-Task Learning (MTL) is an adequate method to do so, but usually requires datasets labeled for…
Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention
Jinwoo Hwang, Philipp Benz, Tae-hoon Kim
Improving multi-view aggregation is integral for multi-view pedestrian detection, which aims to obtain a bird's-eye-view pedestrian occupancy map from images captured through a set…
Privacy Safe Representation Learning via Frequency Filtering Encoder
Jonghu Jeong, Minyong Cho, Philipp Benz +4
Deep learning models are increasingly deployed in real-world applications. These models are often deployed on the server-side and receive user data in an information-rich represent…
Investigating Top- White-Box and Transferable Black-box Attack
Chaoning Zhang, Philipp Benz, Adil Karjauv +3
Existing works have identified the limitation of top- attack success rate (ASR) as a metric to evaluate the attack strength but exclusively investigated it in the white-box sett…
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
Philipp Benz, Soomin Ham, Chaoning Zhang +2
Convolutional Neural Networks (CNNs) have become the de facto gold standard in computer vision applications in the past years. Recently, however, new model architectures have been…
Universal Adversarial Training with Class-Wise Perturbations
Philipp Benz, Chaoning Zhang, Adil Karjauv +1
Despite their overwhelming success on a wide range of applications, convolutional neural networks (CNNs) are widely recognized to be vulnerable to adversarial examples. This intrig…
A Brief Survey on Deep Learning Based Data Hiding
Chaoning Zhang, Chenguo Lin, Philipp Benz +3
Data hiding is the art of concealing messages with limited perceptual changes. Recently, deep learning has enriched it from various perspectives with significant progress. In this…
A Survey On Universal Adversarial Attack
Chaoning Zhang, Philipp Benz, Chenguo Lin +3
The intriguing phenomenon of adversarial examples has attracted significant attention in machine learning and what might be more surprising to the community is the existence of uni…
Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
Chaoning Zhang, Philipp Benz, Adil Karjauv +1
The booming interest in adversarial attacks stems from a misalignment between human vision and a deep neural network (DNN), i.e. a human imperceptible perturbation fools the DNN. M…
Towards Robust Data Hiding Against (JPEG) Compression: A Pseudo-Differentiable Deep Learning Approach
Chaoning Zhang, Adil Karjauv, Philipp Benz +1
Data hiding is one widely used approach for protecting authentication and ownership. Most multimedia content like images and videos are transmitted or saved in the compressed form.…
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Chaoning Zhang, Philipp Benz, Dawit Mureja Argaw +5
ResNet or DenseNet? Nowadays, most deep learning based approaches are implemented with seminal backbone networks, among them the two arguably most famous ones are ResNet and DenseN…
CD-UAP: Class Discriminative Universal Adversarial Perturbation
Chaoning Zhang, Philipp Benz, Tooba Imtiaz +1
A single universal adversarial perturbation (UAP) can be added to all natural images to change most of their predicted class labels. It is of high practical relevance for an attack…
Double Targeted Universal Adversarial Perturbations
Philipp Benz, Chaoning Zhang, Tooba Imtiaz +1
Despite their impressive performance, deep neural networks (DNNs) are widely known to be vulnerable to adversarial attacks, which makes it challenging for them to be deployed in se…
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy
Philipp Benz, Chaoning Zhang, Adil Karjauv +1
Convolutional neural networks (CNNs) have made significant advancement, however, they are widely known to be vulnerable to adversarial attacks. Adversarial training is the most wid…
Revisiting Batch Normalization for Improving Corruption Robustness
Philipp Benz, Chaoning Zhang, Adil Karjauv +1
The performance of DNNs trained on clean images has been shown to decrease when the test images have common corruptions. In this work, we interpret corruption robustness as a domai…
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective
Philipp Benz, Chaoning Zhang, In So Kweon
Batch normalization (BN) has been widely used in modern deep neural networks (DNNs) due to improved convergence. BN is observed to increase the model accuracy while at the cost of…
Data from Model: Extracting Data from Non-robust and Robust Models
Philipp Benz, Chaoning Zhang, Tooba Imtiaz +1
The essence of deep learning is to exploit data to train a deep neural network (DNN) model. This work explores the reverse process of generating data from a model, attempting to re…
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations
Chaoning Zhang, Philipp Benz, Tooba Imtiaz +1
A wide variety of works have explored the reason for the existence of adversarial examples, but there is no consensus on the explanation. We propose to treat the DNN logits as a ve…
Fast Perception, Planning, and Execution for a Robotic Butler: Wheeled Humanoid M-Hubo
Moonyoung Lee, Yujin Heo, Jinyong Park +5
As the aging population grows at a rapid rate, there is an ever growing need for service robot platforms that can provide daily assistance at practical speed with reliable performa…
Propose-and-Attend Single Shot Detector
Ho-Deok Jang, Sanghyun Woo, Philipp Benz +2
We present a simple yet effective prediction module for a one-stage detector. The main process is conducted in a coarse-to-fine manner. First, the module roughly adjusts the defaul…