DeHiB: Deep Hidden Backdoor Attack on Semi-supervised Learning via Adversarial Perturbation
Authors: Zhicong Yan, Gaolei Li, Yuan TIan, Jun Wu, Shenghong Li, Mingzhe Chen, H. Vincent Poor10585-10593
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments based on CIFAR10 and CIFAR100 datasets demonstrates the effectiveness and crypticity of the proposed scheme. |
| Researcher Affiliation | Academia | 1 Shanghai Jiao Tong University, Shanghai, China 2 Princeton University, Princeton, USA |
| Pseudocode | Yes | Algorithm 1: Generating poisoned data |
| Open Source Code | No | The paper mentions using 'an open-source Pytorch implementation of Fixmatch' but does not explicitly state that the code for the method described in this paper is open-source or provide a link. |
| Open Datasets | Yes | Extensive experiments based on CIFAR10 and CIFAR100 datasets demonstrates the effectiveness and crypticity of the proposed scheme. (Krizhevsky 2009) |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits for training/validation/test, or detailed splitting methodology) needed to reproduce the data partitioning. It mentions using training images but no explicit splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch implementation' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We employ a standard set of hyperparameters across all experiments (λu = 1, initial learning rate η = 0.003, confidence threshold τ = 0.95, batch size B = 64). ... We use the , ϵ = 32 and perform PGD optimization for 1000 iterations with learning rate of 0.01 which decays every 200 iterations by 0.95. |