Towards Interpreting Vulnerability of Multi-Instance Learning via Customized and Universal Adversarial Perturbations

Multi-instance learning (MIL) is a great paradigm for dealing with complex data and has achieved impressive achievements in a number of fields, including image classification, video anomaly detection, and far more. Each data sample is referred to as a bag containing several unlabeled instances, and the supervised information is only provided at the bag-level. The safety of MIL learners is concerning, though, as we can greatly fool them by introducing a few adversarial perturbations. This can be fatal in some cases, such as when users are unable to access desired images and criminals are attempting to trick surveillance cameras. In this paper, we design two adversarial perturbations to interpret the vulnerability of MIL methods. The first method can efficiently generate the bag-specific perturbation (called customized) with the aim of outsiding it from its original classification region. The second method builds on the first one by investigating the image-agnostic perturbation (called universal) that aims to affect all bags in a given data set and obtains some generalizability. We conduct various experiments to verify the performance of these two perturbations, and the results show that both of them can effectively fool MIL learners. We additionally propose a simple strategy to lessen the effects of adversarial perturbations. Source codes are available at https://github.com/InkiInki/MI-UAP.

Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints

Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach, without any guidances of the background knowledge. To enhance the expression ability of traditional RBMs, in this paper, we propose pairwise constraints restricted Boltzmann machine with Gaussian visible units (pcGRBM) model, in which the learning procedure is guided by pairwise constraints and the process of encoding is conducted under these guidances. The pairwise constraints are encoded in hidden layer features of pcGRBM. Then, some pairwise hidden features of pcGRBM flock together and another part of them are separated by the guidances. In order to deal with real-valued data, the binary visible units are replaced by linear units with Gausian noise in the pcGRBM model. In the learning process of pcGRBM, the pairwise constraints are iterated transitions between visible and hidden units during CD learning procedure. Then, the proposed model is inferred by approximative gradient descent method and the corresponding learning algorithm is designed in this paper. In order to compare the availability of pcGRBM and traditional RBMs with Gaussian visible units, the features of the pcGRBM and RBMs hidden layer are used as input 'data' for K-means, spectral clustering (SP) and affinity propagation (AP) algorithms, respectively. A thorough experimental evaluation is performed with sixteen image datasets of Microsoft Research Asia Multimedia (MSRA-MM). The experimental results show that the clustering performance of K-means, SP and AP algorithms based on pcGRBM model are significantly better than traditional RBMs. In addition, the pcGRBM model for clustering task shows better performance than some semi-supervised clustering algorithms.