A Review on Steganography Techniques

Steganography is the science of hiding a secret message in cover media, without any perceptual distortion of the cover media. Using steganography, information can be hidden in the carrier items such as images, videos, sounds files, text files, while performing data transmission. In image steganography field, it is a major concern of the researchers how to improve the capacity of hidden data into host image without causing any statistically significant modification. Therefore, this paper presents most of the recent works that have been conducted on image steganography field and analyzes them to clarify the strength and weakness points in each work separately in order to be taken in consideration for future works in such field.   

BLIND IMAGE STEGANALYSIS MENGGUNAKAN METODE MODIFIED K-NEAREST NEIGHBORS

Steganalisis merupakan suatu teknik untuk mendeteksi keberadaan informasi atau pesan rahasia yang disembunyikan dalam suatu media. Pada era digital yang berkembang seperti saat ini, media yang bisa digunakan untuk menyembunyikan informasi atau pesan rahasia tersebut adalah file digital seperti file gambar, audio, video, text dan lain sebagainya. Steganalisis dibagi menjadi dua jenis, yaitu blind steganalisis dan spesifik steganalisis. Penelitian ini khusus meneliti tentang blind steganalisis pada file gambar. Pembahasan dalam penelitian ini berisi tentang rancangan proses blind steganalisis yang dapat diimplementasikan menjadi sebuah aplikasi yang dapat mendeteksi keberadaan pesan rahasia yang disembunyikan dengan cara mengenali stegofile dan cover dengan melibatkan hasil contourlet transform untuk ekstraksi fitur dan modified k-nearest neighbor (MKNN) untuk proses klasifikasi. Aplikasi hasil rancangan proses blind steganalisis dikembangkan dengan bahasa pemrograman python. Aplikasi ini diuji dengan beberapa skenario pengujian. Hasilnya aplikasi blind steganalisis yang dikembangkan mempunyai akurasi rata-rata terbaik 73,5

New watermarking methods for digital images.

The phenomenal spread of the Internet places an enormous demand on content-ownership-validation. In this thesis, four new image-watermarking methods are presented. One method is based on discrete-wavelet-transformation (DWT) only while the rest are based on DWT and singular-value-decomposition (SVD) ensemble. The main target for this thesis is to reach a new blind-watermarking-method. Method IV presents such watermark using QR-codes. The use of QR-codes in watermarking is novel. The choice of such application is based on the fact that QR-Codes have errors self-correction-capability of 5% or higher which satisfies the nature of digital-image-processing. Results show that the proposed-methods introduced minimal distortion to the watermarked images as compared to other methods and are robust against JPEG, resizing and other attacks. Moreover, watermarking-method-II provides a solution to the detection of false watermark in the literature. Finally, method IV presents a new QR-code guided watermarking-approach that can be used as a steganography as well. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b183575

Efficiency of LSB steganography on medical information

The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder

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

An Adaptive Steganography Scheme Based on Visual Quality and Embedding Capacity Improvement

In this paper, a steganography technique using LSB substitution and PVD method is presented as an adaptive scheme in the spatial domain. Our method partitions the grayscale image into several non-overlapping blocks with three consecutive pixels. The embedding algorithm can both replace the secret data with the LSBs of the middle pixel and embed it in the difference values between the middle pixel and its two neighboring pixels of the cover-block. The number of secret bits is determined adaptively based on the range divisions for embedding in the difference value. We define a new range division on gray level which takes into account a larger embedding capacity for bits. After the embedding, the proposed method detects the pixels which are sensitive to hyper distortion. Then, the embedding process will be repeated to produce insignificant visual distortion in those pixels. Our experimental results demonstrate that this iterative steganography scheme prevents significant visual distortion into stego-image. The generated PSNR values are higher than the corresponding values of the most commonly used methods, discussed in this study. Furthermore, the experimental results show that the hiding capacity increased enormously when the proposed range division is used. Finally, we illustrate that the method can pass RS and steganalysis detector attacks.DOI:http://dx.doi.org/10.11591/ijece.v4i4.630

Reversible data hiding scheme based on 3-Least significant bits and mix column transform

Steganography is the science of hiding a message signal in a host signal, without any perceptual distortion of the host signal. Using steganography, information can be hidden in the carrier items such as images, videos, sounds files, text files, while performing data transmission. In image steganography field, it is a major concern of the researchers how to improve the capacity of hidden data into host image without causing any statistically significant modification. In this work, we propose a reversible steganography scheme which can hide large amount of information without affecting the imperceptibility aspect of the stego-image and at the same time, it increases the security level of the system through using different method for embedding based on distinct type of transform, called Mix Column Transform. Our experimental results prove the ability of our proposed scheme in balancing among the three critical properties: capacity, security, and imperceptibility

Image forgery detection using deeplearning by recompressing the images

Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%