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2012.01654
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Towards Defending Multiple
ℓ
p
\ell_p
ℓ
p
-norm Bounded Adversarial Perturbations via Gated Batch Normalization
International Journal of Computer Vision (IJCV), 2020
3 December 2020
Aishan Liu
Shiyu Tang
Xinyun Chen
Lei Huang
Zhuozhuo Tu
Xianglong Liu
Dacheng Tao
AAML
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Papers citing
"Towards Defending Multiple $\ell_p$-norm Bounded Adversarial Perturbations via Gated Batch Normalization"
14 / 14 papers shown
Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing
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MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving
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PromptSafe: Gated Prompt Tuning for Safe Text-to-Image Generation
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259
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AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions
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19 Apr 2025
E
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AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models
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CopyrightShield: Enhancing Diffusion Model Security against Copyright Infringement Attacks
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02 Dec 2024
GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing
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382
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LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions
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438
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Towards Robust Physical-world Backdoor Attacks on Lane Detection
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Aishan Liu
Tianyuan Zhang
Yaning Tan
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Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis
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Zeyu Qin
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Baoyuan Wu
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Domain Invariant Adversarial Learning
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1
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