Steganographer detection via a similarity accumulation graph convolutional network

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.

[1]  Jessica J. Fridrich,et al.  Gibbs Construction in Steganography , 2010, IEEE Transactions on Information Forensics and Security.

[2]  Jing Dong,et al.  Deep learning for steganalysis via convolutional neural networks , 2015, Electronic Imaging.

[3]  Yan Liu,et al.  Deep residual learning for image steganalysis , 2018, Multimedia Tools and Applications.

[4]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD 2000.

[5]  Zhi-Hua Zhou,et al.  Multi-instance learning by treating instances as non-I.I.D. samples , 2008, ICML '09.

[6]  Jure Leskovec,et al.  Hierarchical Graph Representation Learning with Differentiable Pooling , 2018, NeurIPS.

[7]  Eric Granger,et al.  Multiple instance learning: A survey of problem characteristics and applications , 2016, Pattern Recognit..

[8]  Mi Wen,et al.  Steganalysis Over Large-Scale Social Networks With High-Order Joint Features and Clustering Ensembles , 2016, IEEE Transactions on Information Forensics and Security.

[9]  Jessica J. Fridrich,et al.  Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT , 2015, IEEE Transactions on Information Forensics and Security.

[10]  Tomás Pevný,et al.  Using Neural Network Formalism to Solve Multiple-Instance Problems , 2017, ISNN.

[11]  Seokjun Seo,et al.  Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification , 2017, IJCAI.

[12]  Jing Dong,et al.  SSGAN: Secure Steganography Based on Generative Adversarial Networks , 2017, PCM.

[13]  M Tavassoli Kejani,et al.  Graph Convolution Networks with manifold regularization for semi-supervised learning , 2020, Neural Networks.

[14]  Tomás Pevný,et al.  A new paradigm for steganalysis via clustering , 2011, Electronic Imaging.

[15]  Thomas G. Dietterich,et al.  Solving the Multiple Instance Problem with Axis-Parallel Rectangles , 1997, Artif. Intell..

[16]  Yun Q. Shi,et al.  Structural Design of Convolutional Neural Networks for Steganalysis , 2016, IEEE Signal Processing Letters.

[17]  Tomás Pevný,et al.  Batch steganography in the real world , 2012, MM&Sec '12.

[18]  Angshul Majumdar,et al.  Graph structured autoencoder , 2018, Neural Networks.

[19]  Jessica J. Fridrich,et al.  Universal distortion function for steganography in an arbitrary domain , 2014, EURASIP Journal on Information Security.

[20]  Yanli Ren,et al.  Efficient steganographer detection over social networks with sampling reconstruction , 2018, Peer Peer Netw. Appl..

[21]  Jiangqun Ni,et al.  Deep Learning Hierarchical Representations for Image Steganalysis , 2017, IEEE Transactions on Information Forensics and Security.

[22]  Tomás Pevný,et al.  Identifying a steganographer in realistic and heterogeneous data sets , 2012, Other Conferences.

[23]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[24]  Fadi Dornaika,et al.  Learning a discriminant graph-based embedding with feature selection for image categorization , 2019, Neural Networks.

[25]  Jessica J. Fridrich,et al.  Content-Adaptive Steganography by Minimizing Statistical Detectability , 2016, IEEE Transactions on Information Forensics and Security.

[26]  Tomás Pevný,et al.  Merging Markov and DCT features for multi-class JPEG steganalysis , 2007, Electronic Imaging.

[27]  Pierre Vandergheynst,et al.  Geometric Deep Learning: Going beyond Euclidean data , 2016, IEEE Signal Process. Mag..

[28]  Xiaosheng Zhuang,et al.  Fast Haar Transforms for Graph Neural Networks , 2019, Neural Networks.

[29]  Jessica J. Fridrich,et al.  Steganalysis of JPEG images using rich models , 2012, Other Conferences.

[30]  Tomás Pevný,et al.  Statistically undetectable jpeg steganography: dead ends challenges, and opportunities , 2007, MM&Sec.

[31]  Minh Le Nguyen,et al.  DGCNN: A convolutional neural network over large-scale labeled graphs , 2018, Neural Networks.

[32]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[33]  Mingjie Zheng,et al.  Steganographer Detection based on Multiclass Dilated Residual Networks , 2018, ICMR.

[34]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[35]  Zhanxing Zhu,et al.  Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.

[36]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[37]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[38]  Luiz Eduardo Soares de Oliveira,et al.  Multiple instance learning for histopathological breast cancer image classification , 2019, Expert Syst. Appl..

[39]  Tomás Pevný,et al.  "Break Our Steganographic System": The Ins and Outs of Organizing BOSS , 2011, Information Hiding.

[40]  Andrew D. Ker,et al.  The Steganographer is the Outlier: Realistic Large-Scale Steganalysis , 2014, IEEE Transactions on Information Forensics and Security.

[41]  Yun Q. Shi,et al.  Using Statistical Image Model for JPEG Steganography: Uniform Embedding Revisited , 2015, IEEE Transactions on Information Forensics and Security.