494 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
Universal Image Steganalytic Method
In the paper we introduce a new universal steganalytic method in JPEG file format that is detecting well-known and also newly developed steganographic methods. The steganalytic model is trained by MHF-DZ steganographic algorithm previously designed by the same authors. The calibration technique with the Feature Based Steganalysis (FBS) was employed in order to identify statistical changes caused by embedding a secret data into original image. The steganalyzer concept utilizes Support Vector Machine (SVM) classification for training a model that is later used by the same steganalyzer in order to identify between a clean (cover) and steganographic image. The aim of the paper was to analyze the variety in accuracy of detection results (ACR) while detecting testing steganographic algorithms as F5, Outguess, Model Based Steganography without deblocking, JP Hide&Seek which represent the generally used steganographic tools. The comparison of four feature vectors with different lengths FBS (22), FBS (66) FBS(274) and FBS(285) shows promising results of proposed universal steganalytic method comparing to binary methods
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
Local descriptors based on the image noise residual have proven extremely
effective for a number of forensic applications, like forgery detection and
localization. Nonetheless, motivated by promising results in computer vision,
the focus of the research community is now shifting on deep learning. In this
paper we show that a class of residual-based descriptors can be actually
regarded as a simple constrained convolutional neural network (CNN). Then, by
relaxing the constraints, and fine-tuning the net on a relatively small
training set, we obtain a significant performance improvement with respect to
the conventional detector
Aligned and Non-Aligned Double JPEG Detection Using Convolutional Neural Networks
Due to the wide diffusion of JPEG coding standard, the image forensic
community has devoted significant attention to the development of double JPEG
(DJPEG) compression detectors through the years. The ability of detecting
whether an image has been compressed twice provides paramount information
toward image authenticity assessment. Given the trend recently gained by
convolutional neural networks (CNN) in many computer vision tasks, in this
paper we propose to use CNNs for aligned and non-aligned double JPEG
compression detection. In particular, we explore the capability of CNNs to
capture DJPEG artifacts directly from images. Results show that the proposed
CNN-based detectors achieve good performance even with small size images (i.e.,
64x64), outperforming state-of-the-art solutions, especially in the non-aligned
case. Besides, good results are also achieved in the commonly-recognized
challenging case in which the first quality factor is larger than the second
one.Comment: Submitted to Journal of Visual Communication and Image Representation
(first submission: March 20, 2017; second submission: August 2, 2017
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