Influencer Backdoor Attack on Semantic Segmentation
Authors: Haoheng Lan, Jindong Gu, Philip Torr, Hengshuang Zhao
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments verify that a class of a segmentation model can suffer from both near and far backdoor triggers, and demonstrate the real-world applicability of IBA. The code is available at https://github.com/Maxproto/IBA.git. |
| Researcher Affiliation | Academia | 1Dartmouth College 2University of Oxford 3The University of Hong Kong |
| Pseudocode | Yes | Algorithm 1 Nearest Neighbor Injection Require: Mask Y clean, Victim pixels vp, Lower Bound L, Upper Bound U Ainject non-victim pixels Y clean nvp initialize a distance map Mdis for p in Ainject do if L Distance(p, Xvp) U then p 1 , and Mdis = Distance(p, Avictim) elsep 0 return Eligible Injection Area Ainject, Distance Map Mdis |
| Open Source Code | Yes | The code is available at https://github.com/Maxproto/IBA.git. |
| Open Datasets | Yes | The PASCAL VOC 2012 (VOC) (Everingham et al., 2010) dataset includes 21 classes... The Cityscapes (Cordts et, 2016) dataset is a popular dataset that describes complex urban street scenes. |
| Dataset Splits | Yes | The validation and test set contains 1,499, and 1,456 images, respectively. ... The size of training, validation, and test set is 2975, 500, and 1525, respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions models like PSPNet, Deep Lab V3, and Seg Former, and backbones like ResNet-50, but it does not specify any software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | When poisoning training samples with NNI, the upper bound U of the neighbor area is set to 30 on VOC and 60 for Cityscapes, and the lower bound L is all 0. For PRL, the number of pixels being relabeled is set to 50000 for both 2 datasets. The trigger size is set to 15 15 pixels for the VOC dataset and 55 55 for the Cityscapes dataset. All training images from the Cityscapes dataset were rescaled to a shape of 512 1024 prior to the experiments. |