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Works90 from public data
- Text Classification via Large Language Models296
Clue And Reasoning Prompting (CARP) adopts a progressive reasoning strategy tailored to addressing the complex linguistic phenomena involved in text classification, and achieves comparable performances to supervised models with 1,024 examples per class.
- DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation230
A systematic approach is proposed to discover the optimal policies for defending against different backdoor attacks by comprehensively evaluating 71 state-of-the-art data augmentation functions and envision this framework can be a good benchmark tool to advance future DNN backdoor studies.
- Triggerless Backdoor Attack for NLP Tasks with Clean Labels109
A new strategy to perform textual backdoor attack which does not require an external trigger and the poisoned samples are correctly labeled is proposed, which marks the first step towards developing triggerless attacking strategies in NLP.
- Byzantine-Resilient Decentralized Stochastic Gradient Descent88
Ubar is proposed, a novel algorithm to enhance decentralized learning with Byzantine Fault Tolerance that guarantees that each benign node in a decentralized system can train a correct model under very strong Byzantine attacks with an arbitrary number of faulty nodes.
- Privacy-preserving Collaborative Learning with Automatic Transformation Search67
This paper proposes to leverage data augmentation to defeat reconstruction attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract any useful information from the corresponding gradients.
- Stealing Deep Reinforcement Learning Models for Fun and Profit56
This paper presents the first model extraction attack against Deep Reinforcement Learning (DRL), which enables an external adversary to precisely recover a black-box DRL model only from its interaction with the environment.
- EvilEdit: Backdooring Text-to-Image Diffusion Models in One Second53
The EvilEdit is proposed, a training-free and data-free backdoor attack against T2I diffusion models that can backdoor T2I diffusion models within one second with up to 100% success rate and preserves the functionality of the backdoored model using a protected whitelist.
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- Towards efficient privacy-preserving face recognition in the cloud43
This paper proposes an affine transformation, which consists of permutation, diffusion and shift transformations, to protect the privacy of faces and proposes an optimization technique to increase the efficiency of encryption.
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- Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models41
A novel watermark removal attack by combining imperceptible pattern embedding and spatial-level transformations, which can effectively and blindly destroy the memorization of watermarked models to the watermark samples.
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- Processing secure, verifiable and efficient SQL over outsourced database34
This paper proposes a new cloud database model by introducing computation service providers (CSPs), which can accommodate the conventional DBaaS model; the CSPs undertake most of the postprocessing and reconstruction burden for database query.
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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.
- Towards Byzantine-resilient Learning in Decentralized Systems26
Mozi provides a uniform Byzantine-resilient aggregation rule for benign nodes to select the useful parameter updates and filter out the malicious ones in each training iteration to achieve BFT in decentralized learning systems.
- BMIF: Privacy-preserving Blockchain-based Medical Image Fusion23
A privacy-preserving blockchain-based medical image fusion (BMIF) framework based on convolutional neural network and Inception network, and an efficient homomorphic encryption to realize the training of fusion model on encrypted medical data is proposed.
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- Privacy-Aware Image Encryption Based on Logistic Map and Data Hiding22
This paper proposes a novel privacy-aware ROI image encryption (PRIE) scheme based on logistical mapping and data hiding that utilizes salient object detection to automatically, adaptively and accurately detect the privacy region of a given plain image.
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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 2026-10-12. 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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