9 citations · 23 across the 7 of their papers we have counts for
7 papers
ReMatch: Boosting Representation through Matching for Multimodal Retrieval
Qianying Liu, Xiao Liang, Zhiqiang Zhang +6
We present ReMatch, a framework that leverages the generative strength of MLLMs for multimodal retrieval. Previous approaches treated an MLLM as a simple encoder, ignoring its gene…
Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation
Fengfan Zhou, Bangjie Yin, Hefei Ling +2
Face Recognition (FR) models are vulnerable to adversarial examples that subtly manipulate benign face images, underscoring the urgent need to improve the transferability of advers…
Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models
Fengfan Zhou, Qianyu Zhou, Hefei Ling +1
Adversarial attacks on Face Recognition (FR) systems have demonstrated significant effectiveness against standalone FR models. However, their practicality diminishes in complete FR…
Improving the JPEG-resistance of Adversarial Attacks on Face Recognition by Interpolation Smoothing
Kefu Guo, Fengfan Zhou, Hefei Ling +2
JPEG compression can significantly impair the performance of adversarial face examples, which previous adversarial attacks on face recognition (FR) have not adequately addressed. C…
Rethinking Impersonation and Dodging Attacks on Face Recognition Systems
Fengfan Zhou, Qianyu Zhou, Bangjie Yin +4
Face Recognition (FR) systems can be easily deceived by adversarial examples that manipulate benign face images through imperceptible perturbations. Adversarial attacks on FR encom…
Improving Visual Quality and Transferability of Adversarial Attacks on Face Recognition Simultaneously with Adversarial Restoration
Fengfan Zhou, Hefei Ling, Yuxuan Shi +2
Adversarial face examples possess two critical properties: Visual Quality and Transferability. However, existing approaches rarely address these properties simultaneously, leading…