• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.360-373
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• 2017

This paper presents a novel digital video steganalysis scheme against the spatial domain video steganography technology based on a spatial temporal detector (ST_D) that considers both spatial and temporal redundancies of the video sequences simultaneously. Three descriptors are constructed on XY, XT and YT planes respectively to depict the spatial and temporal relationship between the current pixel and its adjacent pixels. Considering the impact of local motion intensity and texture complexity on the histogram distribution of three descriptors, each frame is segmented into non-overlapped blocks that are $8{\times}8$ in size for motion and texture analysis. Subsequently, texture and motion factors are introduced to provide reasonable weights for histograms of the three descriptors of each block. After further weighted modulation, the statistics of the histograms of the three descriptors are concatenated into a single value to build the global description of ST_D. The experimental results demonstrate the great advantage of our features relative to those of the rich model (RM), the subtractive pixel adjacency model (SPAM) and subtractive prediction error adjacency matrix (SPEAM), especially for compressed videos, which constitute most Internet videos.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.366-381
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• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.