11 citations · 17 across the 4 of their papers we have counts for
7 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,…
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…
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…
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.…
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…