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20212026
most citedContent-based Unrestricted Adversarial Attack

10 citations · 43 across the 33 of their papers we have counts for

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Showing 2025Show all

6 papers · 1 filter

cs.CV2025

Dynamic Semantic-Aware Correlation Modeling for UAV Tracking

Xinyu Zhou, Tongxin Pan, Lingyi Hong +5

UAV tracking can be widely applied in scenarios such as disaster rescue, environmental monitoring, and logistics transportation. However, existing UAV tracking methods predominantl…

cs.CV2025

Synthesizing Near-Boundary OOD Samples for Out-of-Distribution Detection

Jinglun Li, Kaixun Jiang, Zhaoyu Chen +4

Pre-trained vision-language models have exhibited remarkable abilities in detecting out-of-distribution (OOD) samples. However, some challenging OOD samples, which lie close to in-…

cs.CV2025

Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment

Kaixun Jiang, Zhaoyu Chen, Haijing Guo +6

Preference alignment in diffusion models has primarily focused on benign human preferences (e.g., aesthetic). In this paper, we propose a novel perspective: framing unrestricted ad…

cs.CL2025

LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops

Jiyuan Fu, Kaixun Jiang, Lingyi Hong +5

Multimodal Large Language Models (MLLMs) have shown great promise but require substantial computational resources during inference. Attackers can exploit this by inducing excessive…

cs.CV2025

VideoPure: Diffusion-based Adversarial Purification for Video Recognition

Kaixun Jiang, Zhaoyu Chen, Jiyuan Fu +3

Recent work indicates that video recognition models are vulnerable to adversarial examples, posing a serious security risk to downstream applications. However, current research has…

cs.CV2025

Boosting Adversarial Transferability with Spatial Adversarial Alignment

Zhaoyu Chen, Haijing Guo, Kaixun Jiang +6

Deep neural networks are vulnerable to adversarial examples that exhibit transferability across various models. Numerous approaches are proposed to enhance the transferability of a…