Search Results for author:
Found 7 papers, 2 papers with code
ADAPT to Robustify Prompt Tuning Vision Transformers
The performance of deep models, including Vision Transformers, is known to be vulnerable to adversarial attacks.
SAIF: Sparse Adversarial and Imperceptible Attack Framework
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal.
Volumetric Propagation Network: Stereo-LiDAR Fusion for Long-Range Depth Estimation
The key idea of our network is to exploit sparse and accurate point clouds as a cue for guiding correspondences of stereo images in a unified 3D volume space.
CD-UAP: Class Discriminative Universal Adversarial Perturbation
Since the proposed attack generates a universal adversarial perturbation that is discriminative to targeted and non-targeted classes, we term it class discriminative universal adversarial perturbation (CD-UAP).
Double Targeted Universal Adversarial Perturbations
This universal perturbation attacks one targeted source class to sink class, while having a limited adversarial effect on other non-targeted source classes, for avoiding raising suspicions.
Data from Model: Extracting Data from Non-robust and Robust Models
We repeat the process of Data to Model (DtM) and Data from Model (DfM) in sequence and explore the loss of feature mapping information by measuring the accuracy drop on the original validation dataset.
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations
We utilize this vector representation to understand adversarial examples by disentangling the clean images and adversarial perturbations, and analyze their influence on each other.
