Search Results for author:
Found 6 papers, 5 papers with code
Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples
In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models.
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
In this work, we propose AdvCLIP, the first attack framework for generating downstream-agnostic adversarial examples based on cross-modal pre-trained encoders.
Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training
Building on these insights, we explore the impacts of data augmentation and gradient regularization on transferability and identify that the trade-off generally exists in the various training mechanisms, thus building a comprehensive blueprint for the regulation mechanism behind transferability.
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label
In this paper, we propose BadHash, the first generative-based imperceptible backdoor attack against deep hashing, which can effectively generate invisible and input-specific poisoned images with clean label.
Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation
The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models.
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer
While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on users, especially for public face images widely spread on social networks.
