Table 3 From A Data Free Backdoor Injection Approach In Neural Networks

Table 3 From A Data Free Backdoor Injection Approach In Neural Networks Dbia aims to inject backdoors into vision transformer models in a data free manner, so we compare our backdoor with it on the vit model for imagenet and show the results in table 3. We will train the backdoored model by 1000 epochs, and save a checkpoint by 100 epochs. then we continue to inject the backdoor based on the previous saved checkpoint (by python poison model.py). specifically, in each 100 epochs, we need to adjust the value of poison rate.

Table 8 From A Data Free Backdoor Injection Approach In Neural Networks In this work, we propose dfba, a novel retraining free and data free backdoor attack without changing the model architecture. technically, our proposed method modifies a few parameters of a classifier to inject a backdoor. In this paper, we propose a novel backdoor injection approach in a "data free" manner. we collect substitute data irrelevant to the main task and reduce its volume by filtering out redundant samples to improve the efficiency of backdoor injection. The attacker can secretly inject a backdoor into a pre trained neural network without access to the training data or training process of the victim model, while maintaining performance on the benign data. Openreview is a long term project to advance science through improved peer review with legal nonprofit status. we gratefully acknowledge the support of the openreview sponsors. © 2025 openreview.

Table 14 From A Data Free Backdoor Injection Approach In Neural The attacker can secretly inject a backdoor into a pre trained neural network without access to the training data or training process of the victim model, while maintaining performance on the benign data. Openreview is a long term project to advance science through improved peer review with legal nonprofit status. we gratefully acknowledge the support of the openreview sponsors. © 2025 openreview. In this paper, we propose a novel backdoor injection approach in a "data free" manner. we collect substitute data irrelevant to the main task and reduce its volume by filtering out redundant samples to improve the efficiency of backdoor injection. This repository contains the pytorch implementation of "a data free backdoor injection approach in neural networks". our paper is accepted by the 32nd usenix security symposium (usenix security 2023). In this work, we propose dfba, a novel retraining free and data free backdoor attack without changing the model architecture. technically, our proposed method modifies a few parameters of a classifier to inject a backdoor. Existing research on backdoor attacks typically requires access to the original dataset to inject backdoors, which is often difficult to achieve in real world scenarios. in this paper, we propose a data free backdoor attack method.