Seek-and-Hide: Adversarial Steganography via Deep Reinforcement Learning.

The goal of image steganography is to hide a full-sized image, termed secret, into another, termed cover. Prior image steganography algorithms can conceal only one secret within one cover. In this paper, we propose an adaptive local image steganography (AdaSteg) system that allows for scale- and location-adaptive image steganography. By adaptively hiding the secret on a local scale, the proposed system makes the steganography more secured, and further enables multi-secret steganography within one single cover. Specifically, this is achieved via two stages, namely the adaptive patch selection stage and secret encryption stage. Given a pair of secret and cover, first, the optimal local patch for concealment is determined adaptively by exploiting deep reinforcement learning with the proposed steganography quality function and policy network. The secret image is then converted into a patch of encrypted noises, resembling the process of generating adversarial examples, which are further encoded to a local region of the cover to realize a more secured steganography. Furthermore, we propose a novel criterion for the assessment of local steganography, and also collect a challenging dataset that is specialized for the task of image steganography, thus contributing to a standardized benchmark for the area. Experimental results demonstrate that the proposed model yields results superior to the state of the art in both security and capacity.

Neural Style Transfer: A Review.

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://osf.io/f8tu4/.

Edge-Sensitive Human Cutout with Hierarchical Granularity and Loopy Matting Guidance.

Human parsing and matting play important roles in various applications, such as dress collocation, clothing recommendation, and image editing. In this paper, we propose a lightweight hybrid model that unifies the fully-supervised hierarchical-granularity parsing task and the unsupervised matting one. Our model comprises two parts, the extensible hierarchical semantic segmentation block using CNN and the matting module composed of guided filters. Given a human image, the segmentation block stage-1 first obtains a primitive segmentation map to separate the human and the background. The primitive segmentation is then fed into stage-2 together with the original image to give a rough segmentation of human body. This procedure is repeated in the stage-3 to acquire a refined segmentation. The matting module takes as input the above estimated segmentation maps and produces the matting map, in a fully unsupervised manner. The obtained matting map is then in turn fed back to the CNN in the first block for refining the semantic segmentation results.