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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
ADBM: Adversarial Diffusion Bridge Model for Reliable Adversarial Purification
Authors: Xiao Li, Wenxuan Sun, Huanran Chen, Qiongxiu Li, Yingzhe He, Jie Shi, Xiaolin Hu
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that ADBM achieved better robustness than Diff Pure under reliable adaptive attacks. In particular, ADBM achieved a 4.4% robustness gain compared with Diff Pure on average on CIFAR-10 (Krizhevsky et al., 2009), while the clean accuracies kept comparable. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science and Technology, Tsinghua University 2Peking University 3Beijing Institute of Technology 4Aalborg University 5Harbin Institute of Technology, Weihai 6Huawei Technologies |
| Pseudocode | No | The paper describes the proposed method, ADBM, and its training and inference processes. However, it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' block with structured steps for a method or procedure. |
| Open Source Code | Yes | Code is available at https://github.com/Lixiao THU/ADBM. |
| Open Datasets | Yes | We conducted comprehensive experiments on popular datasets, including SVHN (Netzer et al., 2011), CIFAR-10 (Krizhevsky et al., 2009), and Tiny Image Net (Le & Yang, 2015), together with a large-scale dataset Image Net-100 |
| Dataset Splits | Yes | Consistent with Nie et al. (2022), we conducted the adaptive attack three times on a subset of 512 randomly sampled images from the test set of CIFAR-10. |
| Hardware Specification | Yes | All experiments were run using Py Torch 1.12.1 and CUDA 11.3 on 4 NVIDIA 3090 GPUs. |
| Software Dependencies | Yes | All experiments were run using Py Torch 1.12.1 and CUDA 11.3 on 4 NVIDIA 3090 GPUs. |
| Experiment Setup | Yes | The adversarial noise was computed in the popular norm-ball setting ϵa 8/255. When computing ϵa, we used PGD with three iteration steps and a step size of 8/255... The finetuning steps were set to 30K... In each fine-tuning step, the value of T in Eq. (9) was uniformly sampled from 100 to 200. Unless otherwise specified, the forward diffusion steps were set to be 100 for SVHN and CIFAR-10 and 150 for Tiny-Image Net and Image Net-100, respectively. The reverse sampling steps were set to be five. The reverse process used a DDIM sampler. We used the Adam optimizer (Kingma & Ba, 2015) and incorporated the exponential moving average of models, with the average rate being 0.999. The batch size was set to 128 for SVHN and CIFAR-10, 112 for Tiny-Image Net, and 64 for Image Net-100 (due to memory constraints). |