arXiv subjects

Xiaoyuan Yang

Publications and source records attributed to Xiaoyuan Yang.

23 records · Page 2Linked to original sources

arXiv2019

Recent Advances of Image Steganography with Generative Adversarial Networks

In the past few years, the Generative Adversarial Network (GAN) which proposed in 2014 has achieved great success. GAN has achieved many research results in the field of computer vision and natural language processing. Image steganography is dedicated to hiding secret messages in digital images, and has achieved the purpose of covert communication. Recently, research on image steganography has demonstrated great potential for using GAN and neural networks. In this paper we review different strategies for steganography such as cover modification, cover selection and cover synthesis by GANs, and discuss the characteristics of these methods as well as evaluation metrics and provide some possible future research directions in image steganography.

cs.CR↗

arXiv2018

The Reincarnation of Grille Cipher: A Generative Approach

In order to keep the data secret, various techniques have been implemented to encrypt and decrypt the secret data. Cryptography is committed to the security of content, i.e. it cannot be restored with a given ciphertext. Steganography is to hiding the existence of a communication channel within a stego. However, it has been difficult to construct a cipher (cypher) that simultaneously satisfy both channel and content security for secure communication. Inspired by the Cardan grille, this paper presents a new generative framework for grille cipher. A digital cardan grille is used for message encryption and decryption. The ciphertext is directly sampled by a powerful generator without an explicit cover. Message loss and prior loss are proposed for penalizing message extraction error and unrealistic ciphertext. Jensen-Shannon Divergence is introduced as new criteria for channel security. A simple practical data-driven grille cipher is proposed using semantic image inpainting and generative adversarial network. Experimental results demonstrate the promising of the proposed method.

cs.CR↗

arXiv2018

Digital Cardan Grille: A Modern Approach for Information Hiding

In this paper, a new framework for construction of Cardan grille for information hiding is proposed. Based on the semantic image inpainting technique, the stego image are driven by secret messages directly. A mask called Digital Cardan Grille (DCG) for determining the hidden location is introduced to hide the message. The message is written to the corrupted region that needs to be filled in the corrupted image in advance. Then the corrupted image with secret message is feeded into a Generative Adversarial Network (GAN) for semantic completion. The adversarial game not only reconstruct the corrupted image , but also generate a stego image which contains the logic rationality of image content. The experimental results verify the feasibility of the proposed method.

cs.MM↗

arXiv2012

Explore what-if scenarios with SONG: Social Network Write Generator

Online Social Networks (OSNs) have witnessed a tremendous growth the last few years, becoming a platform for online users to communicate, exchange content and even find employment. The emergence of OSNs has attracted researchers and analysts and much data-driven research has been conducted. However, collecting data-sets is non-trivial and sometimes it is difficult for data-sets to be shared between researchers. The main contribution of this paper is a framework called SONG (Social Network Write Generator) to generate synthetic traces of write activity on OSNs. We build our framework based on a characterization study of a large Twitter data-set and identifying the important factors that need to be accounted for. We show how one can generate traces with SONG and validate it by comparing against real data. We discuss how one can extend and use SONG to explore different `what-if' scenarios. We build a Twitter clone using 16 machines and Cassandra. We then show by example the usefulness of SONG by stress-testing our implementation. We hope that SONG is used by researchers and analysts for their own work that involves write activity.

cs.SI↗

arXiv2011

Deep Diving into BitTorrent Locality

A substantial amount of work has recently gone into localizing BitTorrent traffic within an ISP in order to avoid excessive and often times unnecessary transit costs. Several architectures and systems have been proposed and the initial results from specific ISPs and a few torrents have been encouraging. In this work we attempt to deepen and scale our understanding of locality and its potential. Looking at specific ISPs, we consider tens of thousands of concurrent torrents, and thus capture ISP-wide implications that cannot be appreciated by looking at only a handful of torrents. Secondly, we go beyond individual case studies and present results for the top 100 ISPs in terms of number of users represented in our dataset of up to 40K torrents involving more than 3.9M concurrent peers and more than 20M in the course of a day spread in 11K ASes. We develop scalable methodologies that permit us to process this huge dataset and answer questions such as: "\emph{what is the minimum and the maximum transit traffic reduction across hundreds of ISPs?}", "\emph{what are the win-win boundaries for ISPs and their users?}", "\emph{what is the maximum amount of transit traffic that can be localized without requiring fine-grained control of inter-AS overlay connections?}", "\emph{what is the impact to transit traffic from upgrades of residential broadband speeds?}".

cs.NI↗