Steganography in IoT: Information Hiding with Joystick and Touch Sensors.

This paper describes a multi-secret steganographic system for the Internet-of-Things. It uses two user-friendly sensors for data input: thumb joystick and touch sensor. These devices are not only easy to use, but also allow hidden data entry. The system conceals multiple messages into the same container, but with different algorithms. The embedding is realized with two methods of video steganography that work on mp4 files, namely, videostego and metastego. These methods were chosen because of their low complexity so that they may operate smoothly in environments with limited resources. It is possible to replace the suggested sensors with others that offer similar functionality.

Steganography in IoT: Information Hiding with APDS-9960 Proximity and Gestures Sensor.

This article describes a steganographic system for IoT based on an APDS-9960 gesture sensor. The sensor is used in two modes: as a trigger or data input. In trigger mode, gestures control when to start and finish the embedding process; then, the data come from an external source or are pre-existing. In data input mode, the data to embed come directly from the sensor that may detect gestures or RGB color. The secrets are embedded in time-lapse photographs, which are later converted to videos. Selected hardware and steganographic methods allowed for smooth operation in the IoT environment. The system may cooperate with a digital camera and other sensors.

Eigenfaces-Based Steganography.

In this paper we propose a novel transform domain steganography technique-hiding a message in components of linear combination of high order eigenfaces vectors. By high order we mean eigenvectors responsible for dimensions with low amount of overall image variance, which are usually related to high-frequency parameters of image (details). The study found that when the method was trained on large enough data sets, image quality was nearly unaffected by modification of some linear combination coefficients used as PCA-based features. The proposed method is only limited to facial images, but in the era of overwhelming influence of social media, hundreds of thousands of selfies uploaded every day to social networks do not arouse any suspicion as a potential steganography communication channel. From our best knowledge there is no description of any popular steganography method that utilizes eigenfaces image domain. Due to this fact we have performed extensive evaluation of our method using at least 200 000 facial images for training and robustness evaluation of proposed approach. The obtained results are very promising. What is more, our numerical comparison with other state-of-the-art algorithms proved that eigenfaces-based steganography is among most robust methods against compression attack. The proposed research can be reproduced because we use publicly accessible data set and our implementation can be downloaded.

Imagechain-Application of Blockchain Technology for Images.

Imagechain is a cryptographic structure that chain digital images with hash links. The most important feature, which differentiates it from blockchain, is that the pictures are not stored inside the blocks. Instead, the block and the image are combined together in the embedding process. Therefore, the imagechain is built from standard graphic files that may be used in the same way as any other image, but additionally, each of them contains a data block that links it to a previous element of the chain. The presented solution does not require any additional files except the images themselves. It supports multiple file formats and embedding methods, which makes it portable and user-friendly. At the same time, the scheme provides a high level of security and resistance to forgery. This is achieved by hashing the whole file with embedded data, so the image cannot be altered or removed from the chain without losing integrity. This article describes the basic concept of an imagechain together with building blocks and applications. The two most important issues are embedding methods and block structure.

Distributed Steganography in PDF Files-Secrets Hidden in Modified Pages.

This paper shows how to diffuse a message and hide it in multiple PDF files. Presented method uses dereferenced objects and secret splitting or sharing algorithms. It is applicable to various types of PDF files, including text documents, presentations, scanned images etc. Because hiding process is based on structure manipulation, the solution may be easily combined with content-dependent steganographic techniques. The hidden pages are not visible in typical application usage, which was tested with seven different programs.