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

Steganography and Steganalysis : Different Approaches

Steganography is the technique of hiding confidential information within any media. Steganography is often confused with cryptography because the two are similar in the way that they both are used to protect confidential information. The difference between the two is in the appearance in the processed output; the output of steganography operation is not apparently visible but in cryptography the output is scrambled so that it can draw attention. Steganlysis is process to detect of presence of steganography. In this article we have tried to elucidate the different approaches towards implementation of steganography using ‘multimedia’ file (text, static image, audio and video) and Network IP datagram as cover. Also some methods of steganalysis will be discussed

Detecting covert communication channels in raster images

Digital image steganography is a method for hiding secret messages within everyday Internet communication channels. Such covert communications provide protection for communications and exploit the opportunities available in digital media. Digital image steganography makes the nature and content of a message invisible to other users by taking ordinary internet artefacts and using them as cover objects for the messages. In this paper we demonstrate the capability with raster image files and discuss the challenges of detecting such covert communications. The contribution of the research is community awareness of covert communication capability in digital media and the motivation for including such checks in any investigatory analysis

An Analysis of Perturbed Quantization Steganography in the Spatial Domain

Steganography is a form of secret communication in which a message is hidden into a harmless cover object, concealing the actual existence of the message. Due to the potential abuse by criminals and terrorists, much research has also gone into the field of steganalysis - the art of detecting and deciphering a hidden message. As many novel steganographic hiding algorithms become publicly known, researchers exploit these methods by finding statistical irregularities between clean digital images and images containing hidden data. This creates an on-going race between the two fields and requires constant countermeasures on the part of steganographers in order to maintain truly covert communication. This research effort extends upon previous work in perturbed quantization (PQ) steganography by examining its applicability to the spatial domain. Several different information-reducing transformations are implemented along with the PQ system to study their effect on the security of the system as well as their effect on the steganographic capacity of the system. Additionally, a new statistical attack is formulated for detecting ± 1 embedding techniques in color images. Results from performing state-of-the-art steganalysis reveal that the system is less detectable than comparable hiding methods. Grayscale images embedded with message payloads of 0.4bpp are detected only 9% more accurately than by random guessing, and color images embedded with payloads of 0.2bpp are successfully detected only 6% more reliably than by random guessing

A comparative study of steganography using watermarking and modifications pixels versus least significant bit

This article presents a steganography proposal based on embedding data expressed in base 10 by directly replacing the pixel values from images red, green blue (RGB) with a novel compression technique based on watermarks. The method considers a manipulation of the object to be embedded through a data compression triple process via LZ77 and base 64, watermark from low-quality images, embedded via discrete wavelet transformation-singular value decomposition (DWT-SVD), message embedded by watermark is recovered with data loss calculated, the watermark image and lost data is compressed again using LZ77 and base 64 to generate the final message. The final message is embedded in portable network graphic (PNG) images taken from the Microsoft common objects in context (COCO), ImageNet and uncompressed color image database (UCID) datasets, through a filtering process pixel of the images, where the selected pixels expressed in base 10, and the final message data is embedded by replacing units’ position of each pixel. In experimentation results an average of 40 dB in peak signal noise to ratio (PSNR) and 0.98 in the similarity structural index metric (SSIM) evaluation were obtained, and evasion steganalysis rates of up to 93% for stego-images, the data embedded average is 3.2 bpp

Hiding Data and Detecting Hidden Data in Raw Video Components Using SIFT Points

Steganography is a science of hiding data in a medium whereas steganalysis is composed of attacks to find the hidden data in a cover medium. Since hiding data in a text file would disturb the coherence of the text or make it suspicious, systematically changing pixels of a visual is a more common method. This process is performed on pixels that are spatially (and/or temporally, for video components) distant from each other so that a viewer\u27s eye can be deceived. Online media are subject to modification such as compression, resolution change, visual modifications, and such which makes Scale Invariant Feature Transform (SIFT) points appropriate candidates for steganography. The current paper has two aims: the first is to propose a method that uses the SIFT points of a video for steganography. The second aim is to use Convolutional Neural Networks (CNN) as a steganalysis tool to detect the suspicious pixels of a video. The results indicate that the proposed steganography method is effective because it yields higher peak signal-to-noise ratio (PSNR = 95.41 dB) compared to other techniques described in cybersecurity literature, and CNN cannot detect hidden data with much success due to its 52% accuracy rate