Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Protecting Model Adaptation from Trojans in the Unlabeled Data
Authors: Lijun Sheng, Jian Liang, Ran He, Zilei Wang, Tieniu Tan
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments across commonly used benchmarks and adaptation methods demonstrate the effectiveness of DIFFADAPT. |
| Researcher Affiliation | Academia | 1 University of Science and Technology of China 2 NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences 3 University of Chinese Academy of Sciences 4 Nanjing University |
| Pseudocode | No | The paper describes the defense method DIFFADAPT through textual descriptions and a framework illustration in Fig. 2, but it does not include a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Code https://github.com/Tom Sheng21/Diff Adapt |
| Open Datasets | Yes | We evaluate our framework on three commonly used model adaptation benchmarks from image classification tasks. Office (Saenko et al. 2010) is a classic model adaptation dataset... Office Home (Venkateswara et al. 2017) is a popular dataset... Domain Net (Peng et al. 2019) is a large-size challenging benchmark... |
| Dataset Splits | Yes | In our experiments, we divide 80% of the target domain samples as the unlabeled training set for adaptation and the remaining 20% as the test set for metric calculation. |
| Hardware Specification | No | The paper mentions using |
| Software Dependencies | No | The paper mentions using |
| Experiment Setup | Yes | For all experiments, we simply set the noise pixels to be sampled from a uniform distribution [0.25, 0.25]. DIFFADAPT is a plug-and-play defense method for existing model adaptation algorithms, so no additional hyperparameters are introduced. Other details are consistent with the official settings of the adaptation algorithms. [...] For the non-optimization-based trigger, we use the Hello Kitty trigger in Blended (Chen et al. 2017) directly. The optimization-based trigger is implemented by GAP (Poursaeed et al. 2018) and Linf norm is 0.5 in a 120 120 patch for Office and 100 100 patch for others. |