A Frequency Domain Steganography using Z Transform (FDSZT)
J. K. Mandal
备注：
4 pages, International Workshop on Embedded Computing and Communication System(IWECC 2011), Rajagiri School of Engineering & Technology, 22-23 December 2011, Kochin
Image steganography is art of hiding information onto the cover image. In this proposal a transformed domain based gray scale image authentication/data hiding technique using Z transform (ZT) termed as FDSZT, has been proposed. ZTransform is applied on 2x2 masks of the source image in row major order to transform original sub image (cover image) block to its corresponding frequency domain. One bit of the hidden image is embedded in each mask of the source image onto the fourth LSB of transformed coefficient based on median value of the mask. A delicate handle has also been performed as post embedding operation for proper decoding. Stego sub image is obtained through a reverse transform as final step of embedding in a mask. During the process of embedding, dimension of the hidden image followed by the content of the message/hidden image are embedded. Reverse process is followed during decoding. High PSNR obtained for various images conform the quality of invisible watermark of FDSZT.
We propose an image steganographic algorithm called EncryptGAN, which disguises private image communication in an open communication channel. The insight is that content transform between two very different domains (e.g., face to flower) allows one to hide image messages in one domain (face) and communicate using its counterpart in another domain (flower). The key ingredient in our method, unlike related approaches, is a specially trained network to extract transformed images from both domains and use them as the public and private keys. We ensure the image communication remain secret except f…
Image steganography camouflages secret messages in images by tampering image contents. There is a natural desire for hiding maximum secret information with the least possible distortions in the host image. This requires an algorithm that intelligently optimizes the capacity keeping the required imperceptibility of the image. This paper presents an image steganography scheme that preserves an adaptively chosen block of dominant coefficients from each Discrete Cosine Transform coefficients, whereas the rest of the coefficients are replaced with normalized secret image pixel values. Secret image …
Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image reversible transformation (S2IRT) scheme for generative steganography. The proposed S2IRT scheme is based on a generative model, i.e., Glow model, which enables a bijective-mapping between latent spa…
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Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）
Image steganography is the art and science of using images as cover for covert communications. With the development of neural networks, traditional image steganography is more likely to be detected by deep learning-based steganalysis. To improve upon this, we propose image steganography network based on style transfer, and the embedding of secret messages can be disguised as image stylization. We embed secret information while transforming the content image style. In latent space, the secret information is integrated into the latent representation of the cover image to generate the stego image…
Three-dimensional (3-D) meshes are commonly used to represent virtual surfaces and volumes. Over the past decade, 3-D meshes have emerged in industrial, medical, and entertainment applications, being of large practical significance for 3-D mesh steganography and steganalysis. In this article, we provide a systematic survey of the literature on 3-D mesh steganography and steganalysis. Compared with an earlier survey [1], we propose a new taxonomy of steganographic algorithms with four categories: 1) two-state domain, 2) LSB domain, 3) permutation domain, and 4) transform domain. Regarding stega…
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Linguistic steganography studies how to hide secret messages in natural language cover texts. Traditional methods aim to transform a secret message into an innocent text via lexical substitution or syntactical modification. Recently, advances in neural language models (LMs) enable us to directly generate cover text conditioned on the secret message. In this study, we present a new linguistic steganography method which encodes secret messages using self-adjusting arithmetic coding based on a neural language model. We formally analyze the statistical imperceptibility of this method and empirical…
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；UC Berkeley（加州大学伯克利分校）；Dolby Laboratories（杜比实验室）
The field of steganography has experienced a surge of interest due to the recent advancements in AI-powered techniques, particularly in the context of multimodal setups that enable the concealment of signals within signals of a different nature. The primary objectives of all steganographic methods are to achieve perceptual transparency, robustness, and large embedding capacity - which often present conflicting goals that classical methods have struggled to reconcile. This paper extends and enhances an existing image-in-audio deep steganography method by focusing on improving its robustness. Th…
Bar-Ilan University（巴伊兰大学）；Facebook AI Research（脸书人工智能研究院）；Carnegie Mellon University（卡内基梅隆大学）
Steganography is the science of hiding a secret message within an ordinary public message, which is referred to as Carrier. Traditionally, digital signal processing techniques, such as least significant bit encoding, were used for hiding messages. In this paper, we explore the use of deep neural networks as steganographic functions for speech data. We showed that steganography models proposed for vision are less suitable for speech, and propose a new model that includes the short-time Fourier transform and inverse-short-time Fourier transform as differentiable layers within the network, thus i…
Style as Cover: Deep Image Steganography via Stylized Transmission
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Computer Science Center（山东省计算中心）；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）
CLPSTNet: A Progressive Multi-Scale Convolutional Steganography Model Integrating Curriculum Learning
Qianan College, North China University of Science and Technology（华北科技大学千安学院）；School of Cyberspace Security, Beijing University of Posts and Telecommunications（北京邮电大学网络安全学院）；College of Science, North China University of Science and Technology（华北科技大学理学院）
The traditional reversible data hiding technique is based on cover image modification which inevitably leaves some traces of rewriting that can be more easily analyzed and attacked by the warder. Inspired by the cover synthesis steganography based generative adversarial networks, in this paper, a novel generative reversible data hiding scheme (GRDH) by image translation is proposed. First, an image generator is used to obtain a realistic image, which is used as an input to the image-to-image translation model with CycleGAN. After image translation, a stego image with different semantic informa…
The challenges faced in the digital world are many, which could be resolved with some biometric recognition methods. These biometric recognition methods are encompassed within watermarking technology, steganography, cryptography, and many other schemes of security. These methods assist in securing digital images with the authentication of their owner. This paper briefly contextualizes the digital watermarking technique, which is referred to as Natural Preserving Transform (NPT) and Hartley Transform, which is endeavored in the face recognition process. The non-blind extraction and quasi-blind …
