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Claim this profileWorks26 from public data
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- Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability27
This paper proposes a novel and severer backdoor attack, TransTroj, which enables the backdoors embedded in PTMs to efficiently transfer in the model supply chain and significantly outperforms SOTA task-agnostic backdoor attacks.
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- Erase and Repair: An Efficient Box-Free Removal Attack on High-Capacity Deep Hiding16
A simple box-free removal attack on deep hiding that does not require any prior knowledge of the deep hiding schemes is proposed and a more powerful removal attack, efficient box- free removal attack (EBRA), which employs image inpainting techniques to remove secret images from container images is designed.
- ELAA: An efficient local adversarial attack using model interpreters16
A novel efficient local adversarial attack (ELAA) using model interpreters to generate severe local perturbations and improve the imperceptibly of the generated adversarial examples is proposed.
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- EGM: An Efficient Generative Model for Unrestricted Adversarial Examples11
A novel decoupled two-step efficient generative model (EGM) is designed, which contains a conditional reference generator and a conditional adversarial transformer and can be also applied to existing attacks to improve their attack success rates, which is of independent interest.
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- VisionGuard: Secure and Robust Visual Perception of Autonomous Vehicles in Practice10
The key of VisionGuard is to leverage the spatiotemporal inconsistency property of PAEs to detect anomalies and it predicts the motion states from historical ones and compares them with the current driving states to identify any motion inconsistency caused by physical attacks.
- Generative adversarial networks with adaptive learning strategy for noise-to-image synthesis10
This work proposes a framework for training GANs with an adaptive learning strategy from simpleness to complexity and demonstrates the proposed method can improve the performance of existing GAns.
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- Text's Armor: Optimized Local Adversarial Perturbation Against Scene Text Editing Attacks6
This paper proposes to actively defeat text editing attacks by designing invisible "armors" for texts in the scene by turning the adversarial vulnerability of DNN-based STE into strength and design local perturbations specifically for texts using an optimized normalization strategy.
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- A Real‐World Study on the Morse Fall Scale and Clinical Judgment Method for Fall Risk in Adult Inpatients4
It is proposed that a two-stage "screening-confirmation" model combining both tools may enhance clinical fall risk management and demonstrate complementary strengths in fall risk assessment.
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- Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding3
A novel Local Deep Hiding (LDH) scheme that significantly increases the embedding rate by hiding large secret images into small local regions of cover images and exhibits superior robustness to common image distortions.
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- Making Theft Useless: Adulteration-Based Protection of Proprietary Knowledge Graphs in GraphRAG Systems2
AURA is a novel framework based on Data Adulteration designed to make any stolen Knowledge Graphs unusable to an adversary, while maintaining 100% fidelity for authorized users with negligible overhead.
- Towards Query-Efficient Black-Box Attacks: A Universal Dual Transferability-Based Framework2
This article proposes a novel black-box attack framework, constructed on a strategy of dual transferability (DT), to perturb the discriminative areas of clean examples within limited queries, and conducts extensive experiments to show that it can significantly improve the query efficiency of existing black- box attacks and attack success rates.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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