166 research outputs found
Steganographer Identification
Conventional steganalysis detects the presence of steganography within single
objects. In the real-world, we may face a complex scenario that one or some of
multiple users called actors are guilty of using steganography, which is
typically defined as the Steganographer Identification Problem (SIP). One might
use the conventional steganalysis algorithms to separate stego objects from
cover objects and then identify the guilty actors. However, the guilty actors
may be lost due to a number of false alarms. To deal with the SIP, most of the
state-of-the-arts use unsupervised learning based approaches. In their
solutions, each actor holds multiple digital objects, from which a set of
feature vectors can be extracted. The well-defined distances between these
feature sets are determined to measure the similarity between the corresponding
actors. By applying clustering or outlier detection, the most suspicious
actor(s) will be judged as the steganographer(s). Though the SIP needs further
study, the existing works have good ability to identify the steganographer(s)
when non-adaptive steganographic embedding was applied. In this chapter, we
will present foundational concepts and review advanced methodologies in SIP.
This chapter is self-contained and intended as a tutorial introducing the SIP
in the context of media steganography.Comment: A tutorial with 30 page
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Computer-generated graphics (CGs) are images generated by computer software.
The~rapid development of computer graphics technologies has made it easier to
generate photorealistic computer graphics, and these graphics are quite
difficult to distinguish from natural images (NIs) with the naked eye. In this
paper, we propose a method based on sensor pattern noise (SPN) and deep
learning to distinguish CGs from NIs. Before being fed into our convolutional
neural network (CNN)-based model, these images---CGs and NIs---are clipped into
image patches. Furthermore, three high-pass filters (HPFs) are used to remove
low-frequency signals, which represent the image content. These filters are
also used to reveal the residual signal as well as SPN introduced by the
digital camera device. Different from the traditional methods of distinguishing
CGs from NIs, the proposed method utilizes a five-layer CNN to classify the
input image patches. Based on the classification results of the image patches,
we deploy a majority vote scheme to obtain the classification results for the
full-size images. The~experiments have demonstrated that (1) the proposed
method with three HPFs can achieve better results than that with only one HPF
or no HPF and that (2) the proposed method with three HPFs achieves 100\%
accuracy, although the NIs undergo a JPEG compression with a quality factor of
75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296;
Sensors 2018, 18(4), 129
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