Does Few-shot Learning Suffer from Backdoor Attacks?

no code implementations • 31 Dec 2023 • Xinwei Liu, Xiaojun Jia, Jindong Gu, Yuan Xun, Siyuan Liang, Xiaochun Cao

However, in this paper, we propose the Few-shot Learning Backdoor Attack (FLBA) to show that FSL can still be vulnerable to backdoor attacks.

Backdoor Attack Few-Shot Learning

A Survey on Transferability of Adversarial Examples across Deep Neural Networks

1 code implementation • 26 Oct 2023 • Jindong Gu, Xiaojun Jia, Pau de Jorge, Wenqain Yu, Xinwei Liu, Avery Ma, Yuan Xun, Anjun Hu, Ashkan Khakzar, Zhijiang Li, Xiaochun Cao, Philip Torr

This survey explores the landscape of the adversarial transferability of adversarial examples.

Image Classification

A Latent Fingerprint in the Wild Database

no code implementations • 3 Apr 2023 • Xinwei Liu, Kiran Raja, Renfang Wang, Hong Qiu, Hucheng Wu, Dechao Sun, Qiguang Zheng, Nian Liu, Xiaoxia Wang, Gehang Huang, Raghavendra Ramachandra, Christoph Busch

Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent.

Benchmarking

Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal

1 code implementation • 17 Jul 2022 • Xinwei Liu, Jian Liu, Yang Bai, Jindong Gu, Tao Chen, Xiaojun Jia, Xiaochun Cao

Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning for good.