Jiansong Zhang, Kejiang Chen, Chuan Qin, Weiming Zhang, Neng H. Yu
{"title":"AAS: Automatic Virtual Data Augmentation for Deep Image Steganalysis","authors":"Jiansong Zhang, Kejiang Chen, Chuan Qin, Weiming Zhang, Neng H. Yu","doi":"10.1109/TDSC.2023.3333913","DOIUrl":null,"url":null,"abstract":"In recent years, steganalysis based on deep learning has evolved rapidly. However, training deep learning models is data-consuming. The models are prone to overfitting when data is limited. Data augmentation is an effective method to mitigate overfitting. Existing data augmentation methods in steganalysis can be categorized into cover enrichment and virtual augmentation. They are used in different stages. Cover enrichment refers to introducing additional cover-stego pairs in some ways, which is performed prior to training. In contrast, virtual augmentation augments data during training. Existing virtual augmentation methods are designed heuristically and rely on expert knowledge. In this paper, we propose the first automatic virtual data augmentation method for steganalysis. Specifically, we design an augmentation network that augments cover and stego images by intelligently adding noises. The augmentation network is trained adversarially with the steganalyzer to generate diverse data. Meanwhile, a “class-invariant” module prevents the augmentation network from changing the original data distribution too much. A “stabilizer” loss function is designed that keeps the adversarial training stable by constraining the number of noises. The experimental results show that the proposed method outperforms existing virtual augmentation methods. Moreover, combining the proposed method and cover enrichment can further boost performance.","PeriodicalId":13047,"journal":{"name":"IEEE Transactions on Dependable and Secure Computing","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Dependable and Secure Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/TDSC.2023.3333913","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
In recent years, steganalysis based on deep learning has evolved rapidly. However, training deep learning models is data-consuming. The models are prone to overfitting when data is limited. Data augmentation is an effective method to mitigate overfitting. Existing data augmentation methods in steganalysis can be categorized into cover enrichment and virtual augmentation. They are used in different stages. Cover enrichment refers to introducing additional cover-stego pairs in some ways, which is performed prior to training. In contrast, virtual augmentation augments data during training. Existing virtual augmentation methods are designed heuristically and rely on expert knowledge. In this paper, we propose the first automatic virtual data augmentation method for steganalysis. Specifically, we design an augmentation network that augments cover and stego images by intelligently adding noises. The augmentation network is trained adversarially with the steganalyzer to generate diverse data. Meanwhile, a “class-invariant” module prevents the augmentation network from changing the original data distribution too much. A “stabilizer” loss function is designed that keeps the adversarial training stable by constraining the number of noises. The experimental results show that the proposed method outperforms existing virtual augmentation methods. Moreover, combining the proposed method and cover enrichment can further boost performance.
期刊介绍:
The "IEEE Transactions on Dependable and Secure Computing (TDSC)" is a prestigious journal that publishes high-quality, peer-reviewed research in the field of computer science, specifically targeting the development of dependable and secure computing systems and networks. This journal is dedicated to exploring the fundamental principles, methodologies, and mechanisms that enable the design, modeling, and evaluation of systems that meet the required levels of reliability, security, and performance.
The scope of TDSC includes research on measurement, modeling, and simulation techniques that contribute to the understanding and improvement of system performance under various constraints. It also covers the foundations necessary for the joint evaluation, verification, and design of systems that balance performance, security, and dependability.
By publishing archival research results, TDSC aims to provide a valuable resource for researchers, engineers, and practitioners working in the areas of cybersecurity, fault tolerance, and system reliability. The journal's focus on cutting-edge research ensures that it remains at the forefront of advancements in the field, promoting the development of technologies that are critical for the functioning of modern, complex systems.