Steganographer detection via a similarity accumulation graph convolutional network

Abstract

Steganographer detection aims to identify guilty users who conceal secret information in a number of images for the purpose of covert communication in social networks. Existing steganographer detection methods focus on designing discriminative features but do not explore relationship between image features or effectively represent users based on features. In these methods, each image is recognized as an equivalent, and each user is regarded as the distribution of all images shared by the corresponding user. However, the nuances of guilty users and innocent users are difficult to recognize with this flattened method. In this paper, the steganographer detection task is formulated as a multiple-instance learning problem in which each user is considered to be a bag, and the shared images are multiple instances in the bag. Specifically, we propose a similarity accumulation graph convolutional network to represent each user as a complete weighted graph, in which each node corresponds to features extracted from an image and the weight of an edge is the similarity between each pair of images. The constructed unit in the network can take advantage of the relationships between instances so that common patterns of positive instances can be enhanced via similarity accumulations. Instead of operating on a fixed original graph, we propose a novel strategy for reconstructing and pooling graphs based on node features to iteratively operate multiple convolutions. This strategy can effectively address oversmoothing problems that render nodes indistinguishable although they share different instance-level labels. Compared with the state-of-the-art method and other representative graph-based models, the proposed framework demonstrates its effectiveness and reliability ability across image domains, even in the context of large-scale social media scenarios. Moreover, the experimental results also indicate that the proposed network can be generalized to other multiple-instance learning problems.