# ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack Source: [ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attack]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:50.713Z Authors: Dahoon Park, Kon-Woo Kwon, Sunghoon Im, Jaeha Kung Published: 2021-11-01T16:44:20.000Z ## Abstract In this paper, we present Zero-data Based Repeated bit flip Attack (ZeBRA) that precisely destroys deep neural networks (DNNs) by synthesizing its own attack datasets. Many prior works on adversarial weight attack require not only the weight parameters, but also the training or test dataset in searching vulnerable bits to be attacked. We propose to synthesize the attack dataset, named distilled target data, by utilizing the statistics of batch normalization layers in the victim DNN model. Equipped with the distilled target data, our ZeBRA algorithm can search vulnerable bits in the model without accessing training or test dataset. Thus, our approach makes the adversarial weight attack more fatal to the security of DNNs. Our experimental results show that 2.0x (CIFAR-10) and 1.6x (ImageNet) less number of bit flips are required on average to destroy DNNs compared to the previous attack method. Our code is available at https://github. com/pdh930105/ZeBRA. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()