Adil Karjauv

Latest

• 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
• Data-Free Universal Adversarial Perturbation and Black-Box Attack
• 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
• Revisiting Batch Normalization for Improving Corruption Robustness