126 research outputs found
One-Time Pad Encryption Steganography System
In this paper we introduce and describe a novel approach to adaptive image steganography which is combined with One-Time Pad encryption, and demonstrate the software which implements this methodology. Testing using the state-of-the-art steganalysis software tool StegExpose concludes the image hiding is reliably secure and undetectable using reasonably-sized message payloads (≤25% message bits per image pixel; bpp). Payload image file format outputs from the software include PNG, BMP, JP2, JXR, J2K, TIFF, and WEBP. A variety of file output formats is empirically important as most steganalysis programs will only accept PNG, BMP, and possibly JPG, as the file inputs.
Keywords: steganography, one-time pad, steganalysis, information hiding, digital forensic
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
Statistical pattern recognition for audio-forensics : empirical investigations on the application scenarios audio steganalysis and microphone forensics
Magdeburg, Univ., Fak. für Informatik, Diss., 2013von Christian Krätze
Recent Advances in Steganography
Steganography is the art and science of communicating which hides the existence of the communication. Steganographic technologies are an important part of the future of Internet security and privacy on open systems such as the Internet. This book's focus is on a relatively new field of study in Steganography and it takes a look at this technology by introducing the readers various concepts of Steganography and Steganalysis. The book has a brief history of steganography and it surveys steganalysis methods considering their modeling techniques. Some new steganography techniques for hiding secret data in images are presented. Furthermore, steganography in speeches is reviewed, and a new approach for hiding data in speeches is introduced
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
The free access to large-scale public databases, together with the fast
progress of deep learning techniques, in particular Generative Adversarial
Networks, have led to the generation of very realistic fake content with its
corresponding implications towards society in this era of fake news. This
survey provides a thorough review of techniques for manipulating face images
including DeepFake methods, and methods to detect such manipulations. In
particular, four types of facial manipulation are reviewed: i) entire face
synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv)
expression swap. For each manipulation group, we provide details regarding
manipulation techniques, existing public databases, and key benchmarks for
technology evaluation of fake detection methods, including a summary of results
from those evaluations. Among all the aspects discussed in the survey, we pay
special attention to the latest generation of DeepFakes, highlighting its
improvements and challenges for fake detection.
In addition to the survey information, we also discuss open issues and future
trends that should be considered to advance in the field
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