Philipp Benz
Philipp Benz
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Investigating Top-k White-Box and Transferable Black-box Attack
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
Towards Robust Deep Hiding Under Non-Differentiable Distortions for Practical Blind Watermarking
Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective
Data-Free Universal Adversarial Perturbation and Black-Box Attack
A Brief Survey on Deep Learning Based Data Hiding, Steganography and Watermarking
A Survey On Universal Adversarial Attack
Universal Adversarial Training with Class-Wise Perturbations
Trade-off Between Accuracy, Robustness, and Fairness of Deep Classifiers
Backpropagating Smoothly Improves Transferability of Adversarial Examples
Is FGSM Optimal or Necessary for L∞ Adversarial Attack?
Towards Simple Yet Effective Transferable Targeted Adversarial Attacks
On Strength and Transferability of Adversarial Examples: Stronger Attack Transfers Better
Stochastic Depth Boosts Transferability of Non-Targeted and Targeted Adversarial Attacks
Universal Adversarial Perturbations Through the Lens of Deep Steganography: A Fourier Perspective
UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Revisiting Batch Normalization for Improving Corruption Robustness
Double Targeted Universal Adversarial Perturbations
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations
Data from Model: Extracting Data from Non-robust and Robust Models
CD-UAP: Class Discriminative Universal Adversarial Perturbation
Propose-and-Attend Single Shot Detector
Revisiting Residual Networks with Nonlinear Shortcuts
Fast Perception, Planning, and Execution for a Robotic Butler: Wheeled Humanoid M-Hubo
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