“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The style-based GAN architecture (StyleGAN) yields state-of-the-art results in data-driven unconditional generative image modeling. We expose and analyze several of its characteristic artifacts, and propose changes in both model architecture and training methods to address them. In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images. In addition to improving image quality, this path length regularizer yields the additional benefit that the generator becomes significantly easier to invert. This makes it possible to reliably attribute a generated image to a particular network. We furthermore visualize how well the generator utilizes its output resolution, and identify a capacity problem, motivating us to train larger models for additional quality improvements. Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

Analyzing and Improving the Image Quality of StyleGAN

/pdf/analyzing-and-improving-the-image-quality-of-stylegan-15vb065a0b.pdf

Review: Digital image steganography: Survey and analysis of current methods

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

A well defined cost function is crucial to steganography under the scenario of minimizing embedding distortion. In this paper, we present a new cost function for spatial image steganography. The proposed cost function is designed by using a high-pass filter to locate the less predictable parts in an image, and then using two low-pass filters to make the low cost values more clustered. Experiments show that the steganographic method with the proposed cost function makes the embedding changes more concentrated in texture regions, and thus achieves a better performance on resisting the state-of-the-art steganalysis over prior works, including HUGO, WOW, and S-UNIWARD.

A new cost function for spatial image steganography

Steganography and steganalysis are important topics in information hiding. Steganography refers to the technology of hiding data into digital media without drawing any suspicion, while steganalysis is the art of detecting the presence of steganography. This paper provides a survey on steganography and steganalysis for digital images, mainly covering the fundamental concepts, the progress of steganographic methods for images in spatial representation and in JPEG format, and the development of the corresponding steganalytic schemes. Some commonly used strategies for improving steganographic se- curity and enhancing steganalytic capability are summarized and possible research trends are discussed.

A survey on image steganography and steganalysis

High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction-error expansion

Histogram shifting (HS) is a useful technique of reversible data hiding (RDH). With HS-based RDH, high capacity and low distortion can be achieved efficiently. In this paper, we revisit the HS technique and present a general framework to construct HS-based RDH. By the proposed framework, one can get a RDH algorithm by simply designing the so-called shifting and embedding functions. Moreover, by taking specific shifting and embedding functions, we show that several RDH algorithms reported in the literature are special cases of this general construction. In addition, two novel and efficient RDH algorithms are also introduced to further demonstrate the universality and applicability of our framework. It is expected that more efficient RDH algorithms can be devised according to the proposed framework by carefully designing the shifting and embedding functions.

General Framework to Histogram-Shifting-Based Reversible Data Hiding

Generative adversarial network has shown to effectively generate artificial samples indiscernible from their real counterparts with a united framework of two subnetworks competing against each other. In this letter, we first propose an automatic steganographic distortion learning framework using a generative adversarial network, which is composed of a steganographic generative subnetwork and a steganalytic discriminative subnetwork. Via alternately training these two oppositional subnetworks, our proposed framework can automatically learn embedding change probabilities for every pixel in a given spatial cover image. The learnt embedding change probabilities can then be converted to embedding distortions, which can be adopted in the existing framework of minimal-distortion embedding. Under this framework, the distortion function is directly related to the undetectability against the oppositional evolving steganalyzer. Experimental results show that with adversarial learning, our proposed framework can effectively evolve from nearly naive random $\pm 1$ embedding at the beginning to much more advanced content-adaptive embedding which tries to embed secret bits in textural regions. The security performance is also steadily improved with increasing training iterations.

Bin Li

Papers

A new cost function for spatial image steganography

A survey on image steganography and steganalysis

High-fidelity reversible data hiding scheme based on pixel-value-ordering and prediction-error expansion

General Framework to Histogram-Shifting-Based Reversible Data Hiding

Automatic Steganographic Distortion Learning Using a Generative Adversarial Network