Coverless image steganography using morphed face recognition based on convolutional neural network

In recent years, information security has become a prime issue of worldwide concern. To improve the validity and proficiency of the image data hiding approach, a piece of state-of-the-art secret information hiding transmission scheme based on morphed face recognition is proposed. In our proposed data hiding approach, a group of morphed face images is produced from an arranged small-scale face image dataset. Then, a morphed face image which is encoded with a secret message is sent to the receiver. The receiver uses powerful and robust deep learning models to recover the secret message by recognizing the parents of the morphed face images. Furthermore, we design two novel Convolutional Neural Network (CNN) architectures (e.g. MFR-Net V1 and MFR-Net V2) to perform morphed face recognition and achieved the highest accuracy compared with existing networks. Additionally, the experimental results show that the proposed schema has higher retrieval capacity and accuracy and it provides better robustness

A High Secured Steganalysis using QVDHC Model

Data compression plays a vital role in data security as it saves memory, transfer speed is high, easy to handle and secure. Mainly the compression techniques are categorized into two types. They are lossless, lossy data compression. The data format will be an audio, image, text or video. The main objective is to save memory of using these techniques is to save memory and to preserve data confidentiality, integrity. In this paper, a hybrid approach was proposed which combines Quotient Value Difference (QVD) with Huffman coding. These two methods are more efficient, simple to implement and provides better security to the data. The secret message is encoded using Huffman coding, while the cover image is compressed using QVD. Then the encoded data is embedded into cover image and transferred over the network to receiver. At the receiver end, the data is decompressed to obtain original message. The proposed method shows high level performance when compared to other existing methods with better quality and minimum error

End-to-end image steganography using deep convolutional autoencoders

Image steganography is used to hide a secret image inside a cover image in plain sight. Traditionally, the secret data is converted into binary bits and the cover image is manipulated statistically to embed the secret binary bits. Overloading the cover image may lead to distortions and the secret information may become visible. Hence the hiding capacity of the traditional methods are limited. In this paper, a light-weight yet simple deep convolutional autoencoder architecture is proposed to embed a secret image inside a cover image as well as to extract the embedded secret image from the stego image. The proposed method is evaluated using three datasets - COCO, CelebA and ImageNet. Peak Signal-to-Noise Ratio, hiding capacity and imperceptibility results on the test set are used to measure the performance. The proposed method has been evaluated using various images including Lena, airplane, baboon and peppers and compared against other traditional image steganography methods. The experimental results have demonstrated that the proposed method has higher hiding capacity, security and robustness, and imperceptibility performances than other deep learning image steganography methods

An image steganography using improved hyper-chaotic Henon map and fractal Tromino

Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively

An adaptive image steganography algorithm based on the use of non-cryptographic hash functions for data extraction

Рассматривается адаптивный алгоритм стеганографического скрытия информации, основанный на итеративном внесении малозначительных искажений в блоки полноцветных изображений-контейнеров и использующий быстродействующие некриптографические хеш-функции для последующего извлечения скрытых данных. Особенностью алгоритма является модификация минимального числа элементов контейнера по сравнению с длиной скрываемых в него фрагментов сообщений, что позволяет увеличить показатели скрытой пропускной способности и снизить визуальную и статистическую заметность скрытых данных. Проводится сравнение алгоритма с современными алгоритмами адаптивного пространственного стегоскрытия в части оценки уровня искажающих изменений контейнеров. Рассматривается вариант повышения пропускной способности алгоритма за счёт мультиплексирования скрытых каналов, использующих общее подмножество элементов контейнера при встраивании в них различных сообщений

A NEW AND ADAPTIVE SECURITY MODEL FOR PUBLIC COMMUNICATION BASED ON CORRELATION OF DATA FRAMES

Recent developments in communication and information technologies, plus the emerging of the Internet of Things (IoT) and machine to machine (M2M) principles, create the need to protect data from multiple types of attacks. In this paper, a secure and high capacity data communication model is proposed to protect the transmitted data based on identical frames between a secret and cover data. In this model, the cover data does not convey any embedded data (as in normal steganography system) or modify the secret message (as in traditional cryptography techniques). Alternatively, the proposed model sends the positions of the cover frames that are identical with the secret frames to the receiver side in order to recover the secret message. One of the significant advantages of the proposed model is the size of the secret key message which is considerably larger than the cover size, it may be even hundred times larger. Accordingly, the experimental results demonstrate a superior performance in terms of the capacity rate as compared to the traditional steganography techniques. Moreover, it has an advantage in terms of the required bandwidth to send the data or the required memory for saving when compared to the steganography methods, which need a bandwidth or memory up to 3-5 times of the original secret message. Where the length of the secret key (positions of the identical frames) that should be sent to the receiver increases by only 25% from the original secret message. This model is suitable for applications with a high level of security, high capacity rate and less bandwidth of communication or low storage devices

Adopt an optimal location using a genetic algorithm for audio steganography

With the development of technologies, most of the users utilizing the Internet for transmitting information from one place to another place. The transmitted data may be affected because of the intermediate user. Therefore, the steganography approach is applied for managing the secret information. Here audio steganography is utilized to maintain the secret information by hiding the image into the audio files. In this work, discrete cosine transforms, and discrete wavelet transform is applied to perform the Steganalysis process. The optimal hiding location has been identified by using the optimization technique called a genetic algorithm. The method utilizes the selection, crossover and mutation operators for selecting the best location. The chosen locations are difficult to predict by unauthorized users because the embedded location is varied from information to information. Then the efficiency of the system ensures the high PSNR, structural similarity index (SSIM), minimum mean square error value and Jaccard, which is evaluated on the audio Steganalysis dataset