We perform classification experiments for a large range of network backbones and several standard datasets on supervised learning and active learning.

Feint behaviors refer to a set of deceptive behaviors in a nuanced manner, which enable players to obtain temporal and spatial advantages over opponents in competitive games.

Fordetailedresults, please referto Table 2 and Table 3 in Appendix A.6.

Toascertain f1 constructed 9,10) returns Eq. (8) forwide NNs,B.1, will Proposition ( ) Universal Statisticsof Fisher Information in Deep Neural Networks: Mean Field Approach.

ROUGE-N scores are the overlapping of n-grams [2] between the generated and referencesequence. Those scores are then averaged overthe whole corpus toreach anoverall quality. For both proposed MMMMI attack methods, shadow models are indispensable. The first hidden layer in the attack model has 256 units and the second hidden layer has20units, bothactivatedbyReLU function. We used resnet-LSTM architecture as the target model architecture.

Compared with the existing membership inference against machine learning classifiers, we focus on the problem that the input and output of the multi-modal models are in different modalities, such as image captioning.