# An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder Source: [An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:55:51.498Z Authors: Liang Liang, Linhai Ma, Linchen Qian, Jiasong Chen Published: 2020-09-17T02:10:36.000Z ## Abstract Deep neural networks (DNNs), especially convolutional neural networks, have achieved superior performance on image classification tasks. However, such performance is only guaranteed if the input to a trained model is similar to the training samples, i.e., the input follows the probability distribution of the training set. Out-Of-Distribution (OOD) samples do not follow the distribution of training set, and therefore the predicted class labels on OOD samples become meaningless. Classification-based methods have been proposed for OOD detection; however, in this study we show that this type of method has no theoretical guarantee and is practically breakable by our OOD Attack algorithm because of dimensionality reduction in the DNN models. We also show that Glow likelihood-based OOD detection is breakable as well. ## 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]()