Steganographic Generative Adversarial Networks

Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier. In the present study, we propose a new model for generating image-like containers based on Deep Convolutional Generative Adversarial Networks (DCGAN). This approach allows to generate more setganalysis-secure message embedding using standard steganography algorithms. Experiment results demonstrate that the new model successfully deceives the steganography analyzer, and for this reason, can be used in steganographic applications.Comment: 15 pages, 10 figures, 5 tables, Workshop on Adversarial Training (NIPS 2016, Barcelona, Spain

Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission

Present-day evolution in communication and information technology dispenses straightforward and effortless access to data, but the most noteworthy condition is the formation of secure communication. Numerous approaches were designed for safety communication. One of the crucial approaches is image steganography. Moreover, provisioning of information security services is arrived at via cryptosystems where cryptosystems make certain the secure messages transmission between the users in an untrustworthy circumstance.  The conventional method of providing encryption and signature is said to be first signing and then encryption, but both the computation and communication costs are found to be high. A certificateless signcryption mechanism is designed to transfer the medical data or images securely. This mechanism will minimize the storage and verification costs of public key certificates. The author of this article proposes a method named Lagrangian recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for transferring the medical data or images securely. In two sections the LRS-HECS method is split. They are medical image steganalysis and certificateless signcryption. First with the Chest X-Ray images obtained as input, a Codeword Correlated Lagrangian Recurrent Neural Network-based image steganography model is applied to generate steg images. Second, to transfer the medical images securely the steg images provided as input is designed a model named a Hyper Elliptic Curve-based Certificateless Signcryption. The issue of providing the integrity and validity of the transmitted medical images and receiver anonymity is addressed by the application of Hyper Elliptic Curve. Chest X-Ray pictures were used in experimental simulations, and the findings showed that the LRS-HECS approach had more advantages over existing state-of-the-art methods in terms of higher peak signal to noise ratio with data integrity and with reduced encryption time and transmission cost

Autoencoder with recurrent neural networks for video forgery detection

Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the learned model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security, and Forensics, January 201